Future Enterprise

The Future of The Internet

2012-01-12T03:33:00.000-08:00

David Hunter Tow – Director of the Future Planet Research Centre, forecasts that within the next decade the Internet and Web may be at risk of splitting into a number of separate entities- fragmenting under technological, national, business and social pressures.

In its place may emerge a network of networks – continuously morphing- linking and fragmenting, with no central dominant domain backbone; instead a disconnected, random structure of networks with information channeled through uncoordinated switching stations and content hubs, controlled by a range of geopolitical, social and enterprise interests.

For authoritarian states such as China, North Korea, Iran and Syria as well as criminal cartels, this will facilitate the expansion of their operations, allowing them to circumvent exposure of illegal activities in much the same way as the current Darknet network.

Darknet- the alternate network of virtual channels that currently operates beneath the backbone of the Internet has long been a place for clandestine operations, by both criminal and state networks. It is also used as a tool by cyber authorities to provide evidence of DDoS, port scanning, worms and other malware; also allowing dissidents from repressive regimes to remain in touch with the outside world, providing protection to whistle blowers and hosting pirated movie and music sites- out of reach of traditional search engines.

Autocratic governments are also maintaining increasingly tight censorship over politically sensitive sites via controlled points of entry to their cyber fiefdoms, even to the extent of distorting current and historical events. Both China and Iran now have plans to establish their own Internet infrastructure to further strengthen the control and censorship of their populations and no doubt other authoritarian states will follow. But this power won’t be limited to dictator-run states. The increasing threat of Internet censorship via the proposed SOPA- Stop Online piracy Action legislation in the US, confirms the threat facing online freedom even within democratic nations and has already motivated opposition by major Web companies concerned about the arbitrary blocking of any site considered deemed to be infringing copyright laws.

At the same time white hat hacker groups plan to launch their own communication satellites linked to a grid of tracking stations in order to avoid such Government surveillance and interference, as discussed at the recent Chaos Communication Congress in Berlin.

But Apple, Facebook, Google, Amazon as well as Cable and Internet TV companies have already begun to fragment the web to support their own Walled Garden strategies of quarantining and manipulating membership data, applications, entertainment, search results and identities. Facebook membership data cannot be transferred to other social sites. Adobe’s Flash software as well as a number of developer applications were banned by Apple, which means the iPhone browser cannot display a large portion of the Internet. Likewise Amazon’s Kindle will only display books on sale or for rent by the company. Google Plus fails to adequately attribute search results to original sources and multiple Ids are banned.

Such social sites have become closed silos, similar in many respects to those of authoritarian sites such as China.

The more this type of restricted, proprietary architecture gains traction on the Web the more it will become fragmented and the easier it will be for criminal groups to exploit, placing the open and egalitarian charter of the future Internet at risk.

But there are compelling reasons why such closed silo strategies, introduced by Governments or Web companies are likely to eventually collapse.

As outlined in previous blogs, physics ordains that information flows cannot be constrained and will eventually spread by pathways of least resistance, driven byconsumer demand, competitive pressure and technological advances. In addition, biological ecosystems with limited genetic variation are the most vulnerable to extinction. Companies within the cyber ecosphere are equally vulnerable- more susceptible to competition and rapid changes in their technological and social environments if open access to innovative ideas and information flows is restricted.

The emergence of the Semantic Web is also a catalyst for greater openness, facilitating the interpretation, linking and application of knowledge stored in millions of discrete databases across the Web. This is a vital advance in fostering greater transparency, flexibility and autonomy within the Cybersphere.

But the battle for web control and Internet supremacy is only just beginning, not only between the US and China but also involving all other nations in the newly emerging multi-polar world. The US still maintains the controlling votes in ICAAN - the Domain management company, despite many attempts to democratise its management.

But now the US will be forced to flex up and stop playing the role of alpha male in an increasingly equal and diverse information world.

By its obsession with maintaining technology dominance of critical assets such as the Web, particularly in a time of global warming, with an urgent need to effectively manage
global resources for all populations, the US is ironically accelerating the rise of alternate Internets and Webs.

China is charging ahead with alternate communication networks, as in most areas of new technology. After all its search engine - Baidu, already has 500 million users - almost as many as Google worldwide. Baidu works hand in glove with the Central Communist Party and is the ultimate arbiter of reality for its users, committed to working within the Government's paranoid censorship parameters constrained by a massive firewall of 50,000 Internet police. But with 200 million bloggers producing trillions of words a day as well as subscribers to RenRen and Seina Weibo- the equivalent of Facebook and Twitter, it’s becoming an increasingly tough call- even for a totalitarian government.

So now the momentum is building for a multi-Internet infrastructure as governments of all colours attempt to impose their will and dominate the evolution of the pre-eminent artefact of our civilisation, which may hold the key to the planet’s survival.

In the short term China cannot replicate the mega optic fibre cable, satellite and server networks of the present Internet, but it can deploy a mesh of alternate channels linking its own network assets to other friendly systems, for example in Africa, South America, Iran and Russia; at the same time constructing a topology complete with their own domain servers. In addition, it will develop its own knowledge hubs while leveraging the existing core public assets such as the priceless science, engineering, social and economic databases of the current Web.

The new US Net Neutrality rules recently introduced to prevent balkanisation are already under heavy fire, with broadband providers prevented from engaging in anti-competitive behaviour by blocking content or slowing access to sites and applications, as Comcast attempted to do in 2007 with the BitTorrent "peer-to-peer" protocol.

But as the pressure to bypass the new rules to allow a multi-speed Internet has increased, so too have the tensions been building between the major Social Web, Broadband and Cloud providers- Google, Apple, Facebook, Cisco, Verizon, Amazon, VMware etc. Cloud vendors have been erecting a new set of proprietary firewalls, with VMware the exception, adopting an open architecture to encourage developers to leverage and extend its technology.

The more such closed architecture with differing operational and security standards gain traction however, the higher the risk that the CloudSphere will eventually become fragmented, less productive and more vulnerable to hacking.

Meanwhile, despite its financial problems, the EU plans to spend billions on boosting broadband speeds to increase productivity and competitiveness. The European Commission will spend 9 billion euros to rollout super-fast broadband infrastructure and services across the European Union to help create a single market for digital public services by 2020 for half its population including- e-health, intelligent energy and cyber security applications, assisting utility companies, construction cooperatives, public authorities and rural users.

New Internet Architecture options are also on the horizon, with a number of innovations in train, forecast to improve the Web’s flexibility while avoiding fragmentation. But these could be put in jeopardy by the US’s intransigence over ceding control.

For example the National Science Foundation has established the Future Internet Architecture program- Nebula, to better secure Internet- Id verification, data safety, mobile access and cloud computing. Google is also setting up a new Web architecture to improve search effectiveness.

At a recent Internet Conference run by the European Paradiso Group, a number of advanced options were discussed including- Internet routing algorithms with quantum options to provide more efficient and secure routing paths; flexible spectrum allocation; a smart Internet environment enabled by networked sensors; a content and context aware Web combined with self-organising and self-adaptive capabilities to provide more autonomy and optimisation.

In addition, the proposed Named Data Networking (NDN) architecture shifts the communication emphasis from today's focus on resource addresses, servers, and hosts, to one oriented to content and context. By identifying data objects instead of just locations, NDN transforms data into the primary Internet focus. While the current Internet secures the channel or path between two communication points, adding data encryption as an extra, NDN will implicitly secure content security and trust.

These and other advances will result in the emergence of Internet Mark 3.0, following its early incarnation as a simple packet data transfer system and then transforming into a pervasive information search powerhouse over the last decade

But Internet 3.0 will only emerge if fragmentation of its infrastructure and the ensuing chaos is avoided

Internet Mark 3.0 will offer- complex multidimensional and ultra-efficient processing and the dissemination of realtime, multi-services and decision-making based on content and context– not just physical objects.

Such capability will drive societal transformation at hyper speed, catalysing - urbanisation, mobility, vastly improved health and education services and all forms of virtual reality, as well as the beginning of a truly symbiotic Web-Human partnership in complex decision-making.

The Future of the Web has been discussed in a number of previous blogs by the author.

In summary-

By 2015 Web 2.0- The Social Web- will have developed into a complex multimedia interweaving of ideas, knowledge and social commentary, connecting over three billion people on the planet.

By 2025, Web 3.0- The Semantic Web- will have made many important contributions to new knowledge through network science, logical inference artificial intelligence. It will be powered by a seamless, computational mesh, enveloping and connecting human and artificial life and will encompass all facets of our social and business lives- always on and available to manage every need.

By 2035, Web 4.0- the Intelligent Web- will be ubiquitous- able to interact with the repository of all available knowledge of human civilisation- past and present, digitally coded and archived for automatic retrieval and analysis. Human intelligence will have co-joined with advanced forms of artificial intelligence, creating a higher or meta-level of knowledge processing. This will be essential for supporting the complex decision-making and problem solving capacity required for civilisation's future survival and progress.

Also by 2035 the last of the enterprise walled gardens will break down and leak like stone walls surrounding an ancient town. Techniques and technologies across the spectrum of knowledge will continue to spread, expand and link in new ways as they always have, bypassing temporary impediments, because that is the physical reality of information and knowledge.

The future Internet will inevitably follow these laws- becoming more open and flexible, using common protocols as enterprises and consumers demand greater flexibility. As an increasing number of data providers begin to implement Tim Bernier Lee’s Linked Data principles, it will transform into the creation of an open global Infosphere containing billions of links and coordinated by the World Wide Web Consortium.

This will offer a blueprint for connecting information from different sources into a single global data repository, with the Global Commons and Public Domain models playing an increasingly important democratic role.

Most importantly the Web will be equally available to and controlled by all nations, under the auspices of a specially constituted UN body, devolving forever away from US control.

But this can only happen if the underlying structural integrity of the Internet and Web is preserved. If managed as a global cooperative project it will result in enormous benefits for the whole of humanity. But if the Future Internet splits and fragments along geopolitical and competitive lines, as its current evolution suggests, then much of its potential benefit for our civilisation and planet will dissipate.

The next evolutionary phase of this pre-eminent human-engineered organism of the 21st century will be critical.

Future Enterprise- Cyberwars

2011-09-13T06:36:00.000-07:00

The Fortune top 2000 companies as well as Governments across the world are under serious cyber attack and it is likely to get much worse.
Cybercrime is a generic term for the illegal incursion and disruption at the national, enterprise and community level, of both cyber and physical assets. Cyber assets include the key information and knowledge resources, including the data, policies, reports, IP, algorithms and applications, programs and operational procedures, that a modern society in the 21st century relies on to operate and manage its business.
Physical assets include an increasing number of everyday objects and services controlled by computers and increasingly connected to the Internet including- infrastructure, manufacturing and production machinery, industrial control and communication centres, security systems, medical devices, electricity grids and meters, vehicles and transport systems as well as billions of consumer and industrial electronic devices.
Cybercrime is a relatively new phenomenon but because of its recent scale and game-changing implications for both government and industry it is rapidly becoming the dominant risk theme of the 21st century.
The opportunity for cyber attacks grows daily as corporations and governments continue to amass information about individuals in complex networks across the Web and at the same time new generations of cyber activists, some motivated purely by money and others by the desire to expose and destabilise corporations and governments, continue to hack into organisational secrets.
No enterprise, no matter how small or benign, will be safe from attack in the future, with an estimated 250,000 site breaches reported in the last few years including- EMC's RSA Security unit, the Public Broadcaster PBS, Sony's PlayStation network, Apple administration password database, the International Monetary Fund, South Korea's largest banks, the Spanish Police, US Senate, Texas Police Department, the CIA, Turkish and Malaysian governments, Google's Gmail, the Nokia forum site and Citibank's Credit Card accounts.
In the latest Norton Cybercrime Report, it was reported that breaches of various types claimed 431 million adult victims last year, with 73% of adults in the US alone incurring estimated financial losses of $US140 billion. As a criminal activity, cyber incursion is now almost as lucrative as the illegal drug trade. The total cost last year, including lost productivity and direct cash losses resulting from cyber attacks associated with viruses, malware and identity theft is estimated at $US 388 billion.
The security firm McAfee report listed a range of cybercrime technologies deployed including- denial of service attacks, malware, spam, phishing, social site engineering, mobile phone viruses, botnets and phone sms Trojan messages. Also more recently, hacking drones- remote controlled aerial vehicles which can automatically detect and compromise wireless networks, by locating a weak spot in a corporate internet connection have been developed. To make matters worse, the first flaws in the advanced encryption standard used for internet banking and financial transactions as well as Government secure transmission, have been discovered.
But most worrying, security experts from McAfee have now discovered the biggest series of cyber attacks to date, involving infiltration of the networks of 72 organisations around the world including- the UN, the governments of the US, Taiwan, India, South Korea, Vietnam and Canada, ASEAN, the International Olympic committee and an array of companies from defence contractors to high-tech enterprises including Google- with most of the victims unaware of the breaches.
This represents a massive loss of economic advantage- possibly the biggest transfer of IP wealth in history. Currently every company in every industry of significant size, with valuable IP, contracts or trade secrets is potentially under attack and this will inevitably extend to smaller organisations such as strategic hi-tech start-ups in the future. At the national level it involves exposure of sensitive state secrets including- policy intentions and decisions covering all levels and functions of Government such as trade, defence and industry policy.
The stakes are huge; a challenge to economies and global markets. From both an enterprise and State perspective therefore this is an intolerable situation; but because it has exploded at such speed, the response to date has largely been fragmented and ineffective.
But this is about much more than ruthless criminal intent to pillage credit cards, steal trade data or bring down unpopular sites. On a global scale, cybercrime has the potential to morph into full blown Cyberwar!
The main players in this game of cat and mouse currently include three broad groups, each with different motivations, although overlapping to a degree.
First- the State sponsored hackers- China, Iran, Russia, Estonia, Israel- recently upping the cyberwar stakes with its Stuxnet attack on the nuclear facilities of Iran, Indonesia, North Korea and Syria. At the same time dictatorial regimes across the world, from Syria to Saudi Arabia have introduced extreme punitive measures to monitor and control access by dissidents, particularly during the Arab Spring. And they have often coerced US and European technology companies to assist them, including Siemens- in the cross-hairs for assisting the autocratic Government of Bahrain track down dissidents.
Second- the White hats- independent freelance hacker groups such as Anonymous/LulzSec. Their aim according to their manifesto is to expose the corruption and greed inherent in the play-books of big business and rogue regimes powered by hyper-capitalism and intent on plundering the natural resources of the planet. They also support whistle-blower groups such as WikiLeaks and social activist groups in general.
Third- the Black hats- with much more clearly defined goals, from overtly criminal to destructive and anarchistic. They are marshalling their attacks primarily on the Midas riches of credit card and financial databases across the globe, at the same time as China and Russia are hacking other Government’s IP, email and trade secrets.
Cyber Hackers now make up a complex substratum of social crime, composed of an ad hoc combination of hackers and security experts, each with a fiercely competitive agenda. But already fragmentation is extending to inter-cyber warfare between these rapidly evolving networks of dysfunctional society, at the same time overlapping with global terrorist groups.
The world's superpowers have already begun to introduce new cyber-policies to desperately protect their intellectual property, infrastructure and financial assets, as well control the flow of information within their populations- but is already bogged down.
The European Convention on Cybercrime is moving at glacial speed because EU governments are reluctant to share sovereign IT information with other powers, even if friendly. The new US Cyber Manifesto has also been stymied. The policy aims to support open access to the Internet while at the same time pursuing a policy of aggressive physical deterrence against any foreign powers such as China and Iran or organisations like WikiLeaks, which attempt to penetrate US computer systems. But this policy is meeting resistance from vested US business interests on issues of regulatory control and government surveillance of business system security.
China on the other hand appears to be going for the jugular. It has established The State Internet Information Office with the express purpose of regulating and controlling its vast Internet population and had even considered building an alternative Internet to sidestep the US controlled ICAAN.
Cybercrime may also be made a lot easier by the ubiquitous application of Cloud technology in the future. Most major corporations and government agencies will be using at least one Cloud to store and process its operational data, leased from Google, Cisco, IBM, Amazon, Microsoft, HP etc. Already several of these clouds including Amazon have been breached and others have had outages. Gaining access to data from a dozen major information sources would be a lot easier than penetrating thousands of individual databases.
Even though most Cloud installations had incorporated security software easily able to ward off rudimentary distributed denial-of-service and hacker attacks, future Cybergent technologies would be much more effective because of superior forensic intelligence.
So the race is on to co-opt the most advanced cyber technology both to gain advantage, but also for prevention. Present day cybercrime technologies however will appear largely primitive within the next few years. The emphasis will shift to the application of much more sophisticated Cyberagent software technology.
The first generation of software agents appeared in the nineties and was used to trawl the Web, applying basic search procedures to locate information resources such as online shopping or travel sites and locating the best prices.
The second generation emerged around five years later. These programs were smarter, incorporating artificial intelligence that enabled them to make decisions more autonomously to meet their operational goals. They were deployed mainly in simulations of interactive population behaviour and interaction in a variety of environments- shopping malls, supply chains as well as disaster and conflict areas. In addition, they possessed superior negotiation and decision logic skills, using Game theory and semantic inferencing techniques.
But the third generation agents will be something else again. These will be based on complementary combinations of advanced AI techniques such as- ‘evolutionary algorithms’, that allow them to constantly improve their skills; 'neural networks' for superior pattern recognition and learning; ‘bayesian logic’ for powerful inferencing capabililty; ‘ant foraging' to help find the most efficient paths through complex network environments and ‘swarm' technology, allowing individual agent intelligence to be amplified by working cooperatively in large groups.
They will increasingly also be capable of tapping into the enormous computational intelligence of the Web, including the public databases of mathematical and scientific algorithms, eventually allowing their intelligence to be amplified by a factor of a hundredfold over previous agent capabilities.
Such agent swarms will also be equipped behaviourally and cognitively to focus on their missions with laser or Zen-like concentration, to the exclusion of everything else, until they have chased down their quarry; whether corporate strategic plans, government covert secrets or nuclear missile blueprints.
This Uber-level of intelligence will transform Agent swarms into formidable cyber strike forces, which could operate under deep cover or in sleeper mode, transforming into harmless chunks of code until a cell and attack was activated and could also replicate rapidly if additional forces were required.
Although this might sound like science fiction, the AI techniques involved, such as evolutionary algorithms, neural networks and swarm architectures have been in common use in business and industry for over ten years. The capacity to harness them in cyber strike force mode is only a matter of time.
But all parties now beginning to understand that the nature of conflict and the balance of world power is shifting with lightning speed, obsoleting overnight the nature of war and traditional economic dominance in a globalised cyber-world. Future conflicts will not be about destroying an enemy armed with billion dollar hi-tech armaments such as tanks, jets and warships, but will be played out largely in future cyberspace.
What value a sophisticated weapons system if it can be disabled by an elite cyber hacker with a Stuxnet-type virus?
What value armies of highly trained soldiers if their command and control centres can be disabled with a few keyboard strokes and a swarm of smart software agents?
What value the trillions of dollars spent on containing Al-Qaeda if the economic and logistical systems supporting the attack can be thrown into disarray by a powerful artificial intelligence algorithm?
But the CEOs of major corporations and military commanders of the major powers are still coming to terms with the mind-blowing ramifications of Cyberwar. Not only would their systems soon be obsolete but so would their command structures.
Adding to the pressure is the impact of global warming and the overuse of the planet’s finite natural resources. Cyberwars are more likely to flourish in times of food and critical resource shortages, with countries and enterprises desperate for inside knowledge to secure access to critical supply information. That time is not far off, with estimates of critical food shortages and rising prices as early as 2013, with a follow on spike in global conflict highly likely.
One thing is certain. From now on Cyberspace will be the new corporate and state battleground and Cybercrime the main risk protagonist.
The threat of all out Cyber war is now an urgent issue that transcends lines between individual enterprises or governments. Unless a global cyber security framework, binding both the private and public sectors can be engineered, a world of disorder will rapidly emerge - a turbulent world, where change has ceased to be beneficial and becomes ultimately destructive.

The Knowledge Universe

2011-07-01T00:07:00.000-07:00

David Hunter Tow- Director of the Future Enterprise Research Centre contends that the dynamics and evolution of the Knowledge Universe are governed by the laws of physics just as the objects in our physical galaxy and universe.

Our Milky Way is a large barred spiral arm galaxy approximately 100,000 light years across in which we have a pantheon of amazing cosmic objects including- at least 200 billion suns and double that number of planets- some just like earth, black holes- including a massive one at its centre equal in power to 4 million suns, numerous dying or dead stars- burnt out white and brown dwarfs neutron stars and remnants of supernovas, trillions of asteroids and meteorites, and vast clouds of hydrogen gas and other molecules giving birth to new stars.

The dynamic links between these galactic entities are primarily a function of the all-pervasive force of gravity, which warps spacetime, creating black holes and initiating the birth and death of stars.

This incredible menagerie does not function as separate objects therefore, but constitutes a gigantic and complex network in a constant state of evolution, emitting radiation from the longest microwave and infrared to the shortest and most energetic x-ray and gamma wavelengths. In turn it is influenced by the other 100-200 billion galaxies that exist in our universe, which in turn may be influenced by other universes or causal patches in a multiverse.

And our small planet, harbouring perhaps the most advanced life form in the universe, is directly or indirectly influenced by all of them.

Our planet’s emerging Knowledge Universe is analogous to this gigantic network of linked galactic objects; a boundless array of information and knowledge objects connected within the networks of the Internet and Web and controlled by its own physical laws.

Information and knowledge objects evolve in a similar way to stars, planets and black holes by adapting to the laws of physics and information within their environments.

They may be loosely classified in terms of a dozen major categories including-

Knowledge Repositories- databases, data warehouses, data centres and modern-day Clouds; Knowledge Processors and Generators- including a vast array of enterprises, web and social sites, specialist software developers as well as community, social, cultural and scientific groups and institutions. These utilise a range of powerful computing devices increasingly linked to the Internet as well as human minds, interconnected via the Web in the form of a powerful computational intelligence.

In addition there exist a plethora of Knowledge Aggregators, Interpreters and Distributors- news feed publishers in both printed and electronic forms, modern day encyclopedia creators such as Wikipedia, and compilers of mathematical, biological, environmental, economic, financial and demographic statistics; utilising networks of all types- wired and wireless, channelling knowledge between and within objects across the Web.

These and many other knowledge object classes and sub-classes constitute a vast network of networks, constantly combining and morphing in unlimited combinations.

A Cloud for example not only stores information, but may process and transmit it as a service. Likewise a social or gaming network may function as a utility, applying database technology such as SQL and many other software tools; but also may manage its knowledge by storing member details and applications via an internal or external Cloud, distributing services via mobile media devices to its members, advertisers and other processing agents. In turn ubiquitous mobile devices - smart phones and tablets, increasingly perform heavy duty processing and provide significant internal storage as well as wireless transmission connected to other networks.

All these objects have a role to play in the knowledge universe menagerie. And in doing so they’re involved in an evolutionary dance of cosmic proportions. But the thing is, this dance is never going to stop and is accelerating in both volume and complexity.

It is estimated that by 2015 the amount of information will quadruple, generated by vast volumes of video transmission as well as countless new applications from the business, social and science research worlds- measured in petabytes.

Knowledge objects are also similar to and interwoven with the cosmic physical forces of the galaxy as they are born, grow, merge, morph, split, regenerate and die, based on the adaptive pressures of their environments. And more and more end up residing in the free public domain.

The evolution of each object is therefore a function of all other knowledge objects in its galaxy, following its own information laws controlled by physical principles and constraints. These include for example the Laws of thermodynamics and entropy, which define the limits of computation and the conversion of data into knowledge. And Shannon’s Laws, which set limits on information channel capacity and transmission.

The laws of physics also includes those governing information and knowledge flows such as the Action Principle – which defines the shortest and least energy intensive path between objects.

The Least Action Principle postulates that any dynamical process, whether the trajectory of a light ray or orbit of a planet, follows a path of least resistance or one which minimises the 'action' or overall energy expended.

Physicist Richard Feynman showed that quantum theory also incorporates a version of the Action Principle and underlies a vast range of processes from physics to linguistics, communication and biology. The evidence suggests a deep connection between this principle based on energy minimisation and self-organising systems including light waves, information flows and natural system topographies, such as the flow of a river.

Information and knowledge is now flowing seamlessly to every corner of the planet and its populations, mediated by the Internet and Web, reaching even the poorest communities in developing countries via cheap PCs, wireless phones and an increasing variety of other mobile devices.

Trying to block or bypass this flow is a pointless exercise and a sure way to hasten an enterprise’s demise. Essential knowledge may be temporarily blocked for example by patents, which protect IP but in 80% of cases are not applied, but used by large enterprises as a competitive blocking strategy. In the process this may deprive poorer populations of essential products such as life-saving drugs.

But regardless, eventually patents run out or are obsoleted by more advanced technologies. This is happening at an increasing rate in all fields - graphene-based electronics, superconducting materials, genetic-based therapies, green technologies, AI and quantum based computing methods.

Enterprise walled gardens therefore eventually break down or leak like a stone wall surrounding an ancient town, as the technology’s lifetime expires and new developments, opportunities and entrepreneurs emerge. Techniques and technologies across the spectrum of knowledge will continue to spread, expand and link in new ways as they always have, bypassing temporary impediments, because that is the physical reality of information and knowledge.

There are many examples of the recent spread and linking of knowledge objects in galactic orbits within the Knowledge universe including-

The Education Galaxy-

The transfer of knowledge is the basis of the education process and is now providing a global flow of free educational material and resources online, including open access courseware. Free courseware is already offered by a number of prestigious tertiary institutions including- The Massachusetts Institute of Technology, Yale and Harvard, as well as free knowledge reference sites such as Wikipedia. And this will accelerate, becoming pervasive in the near future; making it much cheaper and easier for educational resources to reach previously illiterate societies and communities, instead of being monopolised by traditional institutions such as Universities, particularly as a generational shift takes place.

The Knowledge Universe driven by The Action Principle will by 2040 finally allow the developing world to achieve equal status with the developed world in terms of access to knowledge, training and the realisation of human potential.

The Social Galaxy-

It is predicted there will be thousands of social networks within the Knowledge Universe over the next twenty years apart from the scores that exist today such as Facebook, Linkedin, Google Plus, Badoo, Ning, Academia, Craigslist, Foursquare, Plaxo, Yelp, WiserEarth, Meetup, Mebo, Friendster etc, each catering to the needs of specialised groups.

In the near future these will be seamlessly connected by new applications such as Diaspora, avoiding the walled garden effect and allowing individuals to roam at will across the social universe unimpeded.

The Media Galaxy-

In the Media arena the die has been cast. The older print companies are desperately trying to reposition to face of the online revolution. But by 2015 most print media will be forced to radically adapt towards an online multimedia model. Newspapers are already in turmoil with advertising revenues collapsing as traditional classified streams dry up due to online competition.

Traditional news, both local and global, is rapidly being reduced to a stream of headlines with minimal analysis. Special editions and feature articles will continue in reduced quantity, but online short-burst information- text, video and audio streams will become increasingly popular, distributed via multimedia platforms such as the new generation smart phones, tablets and eBooks, already in common use.

By 2020- traditional free to air television channels will also have largely disappeared, along with many cable channels, with television advertising similarly caught in the headlight glare of change. The switch will be to web channels covering every topic- personalised to individual taste- viewable anywhere, anytime and watched primarily on mobile media screens. The personalised channel will be ubiquitous, with news, information, music and video filtered and customised to suit every personal taste.

All print media including magazines and books will also have followed newspapers to a multimedia model distributed over the Web for flexible viewing. The same already applies to music and video. The power of traditional publishers and creative gatekeepers is now being challenged as online stores such as Amazon, Apple and Google and many smaller companies allow any author, song writer or video producer to self-publish globally and cheaply.

The Cloud Galaxy-

The Cloud is a metaphor for shared infrastructure, software and data storage within the web.

Clouds already support a large range of knowledge environments including- social, cultural, business, energy, financial, office, retail, manufacturing, supply chain, booking, engineering, gaming, music, photo, video, media, communications and scientific applications.

Most of the major service and software providers including IBM, EDS, Apple, Google, Amazon, Yahoo, Microsoft and e-Bay still adopt a walled garden approach, providing access to proprietary databases through proprietary Web Application Programming Interfaces-APIs.

APIs, rely on different ID and access mechanisms as well as data in specific formats for example to support music, video, particle collider and human genome information. Therefore APIs have tended to slice the web into separate sources and silos, restricting its full potential.

However In the future Clouds will become more generic and open using common protocols as enterprises demand greater flexibility. But the next evolutionary phase will offer much more- in particular Data Linking. This will promote the sharing of datasets across diverse domains and between business, research and group partners, bringing the full semantic power of the Web into play and changing the face of business forever.

Tim Berniers Lee’s recent publication of Linked Data Principles for connecting structured data on the web, provides a future blueprint for connecting information from different sources into a single global data repository; accessible by generic data browsers and standard database and query languages. An increasing number of data providers have now begun to implement these Linked Data principles, leading to the creation of an open global data space containing billions of links and coordinated by the World Wide Web Consortium.

And so the trendlines are now becoming clear. The Web is advancing as a multi-dimensional medium for the discovery, generation and linking of knowledge in all its forms, leveraging semantic and artificial intelligence. Individual supplier services will obviously continue to multiply, but enterprises will increasingly demand access to open source data clouds as well as most utility services.

Cloud spaces will continue to blend and split, fragment and reform in unlimited combinations and permutations. They will share data as media organisations already do amongst themselves and with countless news aggregators. The divide lines between public and private ownership of application IP will also become fuzzy, with most applications and algorithms over time converting to generic forms- as many critical software tools such as Linux, Java and SQL.

The Global Commons and Public Domain models therefore will play an increasingly important role. They represent a free sharing knowledge marketplace accessible for the global benefit, where everyone wins as value-added services proliferate. Alternate knowledge and social hubs such as the thousands of Wikipedia lookalikes, controlled by consumer groups, will start to compete with and displace the power of the media and Uber-web enterprises such as Google, which will be forced to cede part of its global knowledge control in its own survival self-interest.

The Web will be controlled by all nations via the global commons in conjunction with a specially constituted body such as the present ICAAN, devolving away from US control.

Many companies have tried to go against the evolutionary flow in the past and paid the price – including GM and Ford which continued to produce large gas-guzzling vehicles. They survived the low carbon/electric vehicle revolution only because of taxpayer largesse.

IBM was another that attempted to force the market to accept its large mainframes- against the trend towards small desktop computers and later the internet. IBM almost died but recovered just in time by embracing software and services, and now leveraging its Smart Planet Strategy.

Microsoft has until recently continued to promote desktop computing against the trend to internet and mobile computing and has been caught flat footed. It may survive as it belatedly adapts its office software to the Internet, but not in its previous dominant position.

Nokia was king of mobile phones but failed to see the shift to smarter phones and applications. It has now been forced to merge to survive, with a low likelihood of ever returning to its glory days.

Oracle, Apple and Facebook are busy building walled gardens. Although looking dominant today their longer term survival will also be in jeopardy if they continue their retro strategy against the flow.

The latest 'Smart Planet' paradigm, in which the infrastructure and processes of the planet- whether manufacturing, supply chains, electricity grids, water pipelines or traffic flows, are being re-engineered to optimise performance and achieve greener, more sustainable outcomes, will be the major driver for the enterprise of the future.

The Smart Planet will also demand that decisions be made more rigorously, efficiently, adaptively and therefore largely autonomously, within a radically new networked architecture.

This will be a major disruptive paradigm for many traditional IT companies which will be forced to redesign their applications and services from the ground up. Those that are too slow will be overtaken by the new generation of nimble system developers, not weighed down by legacy systems. The larger software enterprises in particular will struggle to keep up with the constant flow of knowledge and innovation required to survive, after comfortably dominating their market segment for years, as the cycles of change get shorter and shorter.

The flow of information and knowledge according to physical principles will continue at an accelerating rate, but still many companies will try to continue to swim against the flow to their eventual cost.

Within two decades today’s Internet and Web itself will have split into many alternate distributed but connected network descendants, eventually criss-crossing the knowledge universe and supporting autonomously managed worlds with different processing efficiency and reliability requirements.

Software and system developers and suppliers will need to differentiate their products increasingly as focussed value-added services, targeted to specific enterprises and industries. Service applications will therefore be differentiated primarily by the level of value they contribute to the enterprise- not their generic capability.

Enterprises in turn will need to be very agile, not only because of the exponential rise in the diversity and volume of knowledge, but also its potential for interweaving and creating opportunities in countless applications. They will therefore need to keep acutely tuned to the signals from their environment to survive.

As the Knowledge Universe expands and complexifies as a network of networks, with the spread of information and knowledge according to the laws of physics, enterprises will have only one avenue of escape. That is to continually innovate to generate new knowledge in the form of new products and services before the next wave of science and technology innovation overtakes them; just as electric cars, digital photography and smart phones have already obliterated whole sectors of industry in the blink of an eye.

No enterprise can escape this remorseless race. Better to join it rather than putting up a wall which will inevitably crumble.

They will need to run very hard just to survive- just like the Red Queen.

The Big Picture

2011-04-11T19:00:00.000-07:00

David Hunter Tow- Director of the Future Enterprise Research Centre argues that seeing the big picture is essential for the survival of the future enterprise.
Seeing the Big Picture- relating the enterprise’s role within its physical and social environment, will become increasingly vital for its survival in the future. It will not be sufficient to plan one, two or five years ahead. Although near-term planning is essential, understanding the big shifts likely to impact our planet and future civilization, will be essential inputs to creative planning, adaptive agility and risk avoidance.
Take Google for example. Many of its acquisitions such as YouTube and Maps were made with the longer term potential in mind rather than short term profits. These were strategic targets that fitted with Google’s general philosophy and could mesh with its long term goals. It was understood that eventually there was a high likelihood of a major payoff and therefore immediate profitability from these acquisitions were not a priority.

Accurately predicting the longer term future has always been seen as problematic, since the Delphi Oracle was shown to have made her forecasts under the influence of laughing gas from an underground aquifer. So there’s been an assumption that’s it’s an impossible mission and why bother as long as the next three to five year profit forecasts are on track.

Most forecasting textbooks traditionally list a number of well-developed techniques based on- time series projections, regression analysis, Delphi and scenario expert options, artificial neural networks and simulation modelling. But these have usually failed miserably to predict the future in times of abrupt change within the broader physical, social or economic environment; such as recent extreme disasters, the global financial crisis or the Arab democratic revolution.

In fact enterprises- even the biggest, have a poor history in seeing the big picture. For example, IBM, didn’t see the looming shift to personal computers and let slip one of the most strategic opportunities in modern times; handing operating software- DOS, ideal for managing desktop computers, to a small startup- Microsoft. And it almost repeated this failure with the advent of the Internet, ignoring its potential until it realised everyone else had embraced the ability to go online to the world. Only its enormous base of mainframe systems saved IBM from oblivion.
And in more recent times there was Ford and GM. Both went virtually bankrupt and had to be bailed out by the US Government because they would not or could not see the obvious shift in consumer sentiment to smaller cars with lower fuel usage. And then there was Lehman Brothers and Citibank and Fanny Mae which also thought they were invincible and too big to fail.
And the list goes on and on. So what’s the problem?

In all the above cases, enterprise management ignored the signals coming loud and clear from their environments via consumers and customers, through a combination of ignorance and arrogance. In the meantime other more agile companies such as Microsoft and Toyota picked up the signals and exploited the opportunities. But then Microsoft almost lost the plot to Google by not seeing the emerging power of the Internet as the dominant driver of information in today’s society.

In other words, the problem is that many companies, particularly those that are dominant in their industry sectors, begin to believe their own rhetoric; that they can manipulate the market according to their whims and wishes, with consumers eventually falling into line, perhaps with an extra push from of a persuasive enough advertising campaign.
This may work in the short term, but if they continue to fail to adapt and evolve, going against the flow and focussing only on their past history through a prism that becomes increasingly self-reflective, such organisations will eventually lose sight of the big picture and reality. This is despite often employing hundreds of strategic analysts, planners, marketing gurus and forecasters, as well as deploying the most advanced computing systems on the planet.

The bigger the enterprise therefore, the more likely it is to live in a bubble of its own making, believing its own internally generated myths. So despite the use of the latest business intelligence software busily scavenging for patterns generated from past customer and financial data, standard industry forecasts and the odd focus group, the analysis will be virtually useless as guide to an uncertain future and as an adaptation tool in the face of looming disasters.

And inevitably without being aware of the bigger but often more subtle shifts in their global environment, such enterprises eventually end up on the edge of a financial precipice without a safety net.

But still many of the latest forecasting trends reinforce this suicidal behaviour by extrapolating trends or variables from past datasets or building scenarios based on narrow parameters.
To understand the future therefore, it will be essential for an enterprise to also understand and be aware of reality at a far deeper level, beyond traditional business boundaries. Many of the most seemingly complex patterns of reality and life are derived from simple rules, based on the science of fractals, chaos, networks, quantum theory, computation and evolution.

Our increased understanding of simple structures such as the human genome allows us to gain exquisite insight into the enormous complexity of life and the cause of many diseases; while understanding chaos and network theory allows us to better manage ecosystems and improve our prediction of disasters- both natural and man-made.

So being able to see the bigger picture and understand its ramifications is vital for the survival of the future enterprise. But how does a system traditionally steeped in conventional narrowly focussed management techniques change its mindset? The major social and physical drivers are not always as obvious as global warming, globalisation or cyber-revolutions.
One critical part of the process involves integrating disparate, often unrelated sources of information and trends across multiple domains of knowledge and expertise. This goes well beyond traditional business intelligence and analytic techniques and comes under the new category of Macroscopic analysis.

The sciences are increasingly using the lens of the macroscope in innovative ways to support collaborative research and gain big picture perspectives in disciplines such as biology, cosmology, ecology and quantum physics. Macroscopes are flexibly updatable combinations or bundles of cyber infrastructure software, algorithms, web services, computing resources and toolkit plug-ins supporting computational analysis and workflows, capable of facilitating the synthesis of vast amounts of research information from thousands of databases around the world. They perform meta-analyses to discover relevant patterns and make predictions to solve critical puzzles such as the genetic causes of cancer and the nature of dark matter. And then going one step further they combine interdisciplinary trends across for example, astronomy and biology, to create new domains such as astrobiology, to determine the likelihood of other life forms existing within the universe.

Now business is also realising the value of applying the benefits of macroscopes to better see the big picture and avoid disaster.
Business macroscopes will provide a much more holistic view of complex information and knowledge sets, detecting significant risks and trends from multiple, often unrelated sources as in the sciences; derived not just from historical enterprise transactions and analyses, but from disciplines that have never before entered the organisation’s lexicon, such as climate change and social networks.

In other words, macroscopes are like giant biological filter feeders, such as whales. Vast flows of water containing micro-organisms and detritus are constantly pumped through the animal’s filtering system and assessed for their value; with only the residues necessary for the animal’s energy and survival retained. It is a largely an automatic process and so it will be for the enterprise once the architecture and parameters have been established. Instead of water, vast flows of complex information and events will be analysed to determine those fluxes most relevant to the organisation’s well-being and survival.

Implementing the macroscope in business will involve integrating it into the fabric of the future enterprise and its IT support systems. This will be a daunting task, but the templates have already been established in the form of the rigorously tested service-oriented architectures such as the Open Services Gateway initiative and Cyber Infrastructure Shell - OSGi/CIShell, which support the interoperability of applications and services by allowing dynamic plug and play integration of independent web service, algorithm and tool components.

Science used to lag the business community in its use of standard tools and innovative computational practice. Now it’s the reverse. Enterprises need to adopt the insight and rigour that science has had to apply to meet the high standards of proof required by society, based on the scientific method.

So now science and business are partners- in lockstep, applying the same computational methods and intelligence to secure their futures and never losing sight of the big picture.

The Future of Work within the Enterprise Ecosystem

2011-01-16T23:24:00.000-08:00

Director of the Future Enterprise Research Centre-David Hunter Tow, forecasts that within the next two decades, the future architecture guiding the enterprise will dramatically alter traditional work patterns.

By 2020 the traditional notion of an individual's job and work-related role will be recognised as outdated, increasingly mismatched with the fluid requirements of the 21st century. Future productivity outputs will be measured in terms of flexible value-added criteria and contribution to the goals of the organisation linked to social utility, rather than in terms of hours worked on a specific project.

The traditional office will also become redundant as the wireless web expands, allowing information workers- fifty percent of the workforce, to operate from home or local social hubs such as coffee bars, as already occurring- (Ref Future Cities). All such centres will be linked seamlessly via the Internet's multimedia Wireless Grid/Mesh Utility supporting Web and Cloud Infrastructure. This will also enable enormous time and energy savings for workers and the planet in general, having a beneficial impact on the quality of life for millions.

Most tasks, even in the traditional labour-intensive sectors of health, construction, manufacturing and transport will be largely automated or robot-assisted. Projects will be managed and resourced on a real-time basis within the Web's global knowledge network- (Ref Future Web).

Boundaries will then blur between traditional full-time, part-time, contract and volunteering modes of employment as well as between worker and management roles. Most workers will share time between their own creative projects and enterprise applications as already happening, with creativity and innovation recognised as critical work competitive inputs.

Tomorrow's enterprise will be most effectively represented as a decision network model with decisions as nodes and information flows linking the relationships between them. This model offers an extremely powerful mechanism for understanding and optimising the enterprise of the 21st century- extending far beyond current non-adaptive process models.

The enterprise ecosystem’s organisational boundaries and work practices will therefore become increasingly fluid and porous, in synch with the new adaptive network flow architectures. Individuals will move freely between projects, career paths and virtual organisations within the ecosystem; adding value to each enterprise and in turn continuously acquiring new skills, linked to ongoing advanced learning programs. Work patterns will therefore gradually adapt to a model of seamless knowledge flows, generated both by human and web-based algorithms.

The semantic distinctions between workers and management will also disappear with robots performing a large proportion of operational roles without human supervision. The role of unions in the workplace will then have morphed to providing largely advisory, research and cooperative support services.

Concurrently with the above scenarios will be a recognition that the philosophy and architecture of the enterprise of the future will require a major focus on surviving in an increasingly complex environment; requiring the capacity to optimise operations and strategies in shorter and shorter timeframes within a fast changing global cultural, economic, physical and technological environment.

To achieve this goal, artificial and human intelligence will need to merge at both the strategic and operational levels, driven by a need to implement decision-making autonomously with minimal human intervention, as is already occurring in advanced communication and control systems. The genesis of this trend is also becoming apparent in current service-oriented applications including- procurement and supply, resource and financial management and health and lifestyle services, where capitalising on short-term windows of opportunity is paramount.

By 2040, work will relate primarily to the generation of new knowledge and services, by combining human, robot and web intelligence to maximum potential. Most processes will be fully automated both at the operational and strategic level within the context of the Intelligent enterprise. New products and services will be generated from concept to design to production within months, days or hours. Individual creativity and skills will remain in high demand but will increasingly be amplified and modulated within the context of the Web's cooperative decision-making and intelligence capacity.

The survival and success of the enterprise will therefore be contingent on its embedding within the broader cultural environment and norms of the larger community. Business will become an integral component of community culture, with its governance reflecting ethical and sustainable global standards. There will also emerge much greater cooperation rather than competition between enterprises, as globalisation and global warming become the dominant socio-economic drivers.
The days of separating commercial decisions from their social impact will be over.

By 2050, the larger enterprise will evolve as a semi self-organising entity within a larger ecosystem, operating in largely autonomous mode. New knowledge will constantly add value to its evolution, generated through organisational decision processes and knowledge network flows.

The Future Enterprise ecosystem will therefore morph, merge and dissemble in a seamless and endless cycle, generating new processes, knowledge and services to support the global community.

Welcome to a brave new world.

The Smart Business case

2011-01-16T21:22:00.000-08:00

The Director of the Future Enterprise Research Centre- David Hunter Tow, argues the case for a complete reappraisal of the role of the Business Case and the validity of its current methodology.

There is an endemic structural weakness in today’s business case methodology, which is particularly problematic for Information Technology projects. It arises primarily because of the inability of most enterprises to adequately quantify the benefits relating to investment in new services and technologies.

Since the seventies, business and IT management have been stuck in a mindset which hasn’t changed from the time it became obvious that computer hardware and software was continuing to soak up large amounts of an organization’s capital expenditure budget.

And because of the increasing investment required to computerize the operations of a an organization, it occurred to management that it would be a good idea to offer a business case to justify its introduction. From that point to the present day, the mythology relating to measuring the indirect benefits of this expenditure has grown.
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At the beginning most justification was comparatively easy. The case for computerizing the early banking, insurance, manufacturing and retail industries could be easily made, by comparing FTE cost savings from redundant staff with the cost of the computer hardware and software and the much smaller number of operations personnel required.

But then came the next generation of computers- client/server distributed systems, networked technologies, real-time operating environments and software that hid the real cost of regular maintenance, customization and upgrades. So it got harder to justify such systems on a cost savings basis alone, once the original legacy back-office savings had been made.

But everyone knew there were major additional benefits associated with up-to-date information and reporting, faster turnaround of accounts, better customer service and improved management decision-making. And from a government perspective, there would be public benefits as well as the quality of service delivery improved.

But how to translate these other ‘soft’, ‘indirect’, ‘intangible’ benefits, which were obvious to everyone, but apparently fiendishly difficult to pin down, into hard cold cash; that could realistically be factored into the ROI.

And then there emerged a rationalization to solve the problem- a dichotomy. The direct ‘tangible benefits’- those offering obvious direct cost savings, like reducing staff or inventory, were the ones that traditional bookkeepers could quantify and management felt comfortable with.
The indirect ‘intangible benefits’- the fuzzy ones, which of course by now were much bigger than the ‘direct benefits’ and could actually justify a major investment, would remain as best estimates. No-one in their right mind would actually attempt to calculate the value derived from improvements in strategic decision-making or customer satisfaction- and then put their signature to it- would they?..

So gradually the mythology of the intangible, incalculable benefit became embedded in the enterprise psyche.

Managers loved it because they could promote their favorite projects without having to seriously justify them. CIOs loved it because any problems relating to the failure of an application to deliver its promised benefits couldn’t be sheeted home to them. Suppliers loved it because that could maximize their sales of the next big thing; sometimes even writing the business case. And if anyone was silly enough to question their integrity, they could check with the other industry lemmings who had invested in the same magic bullet based on a watertight business case and who would never admit to a competitor they had made a monumental investment error.

And lastly, the high priced guru consultancy firms loved it because it was easy to charge an astronomical fee for a complex business case without actually proving the real payoff; and they couldn’t be blamed if the investment turned out to be a dud, because everyone including the CEO had signed off on it. And everyone knew it was impossible to quantify intangibles anyway.

And so the myth of intangible benefits grew. And as more and more technological advances emerged- the internet, software as a service, content integration, virtualization etc, the percentage of hard tangible benefits that could be offset against costs shrank to 20%, then 10%, then 5%, then zero and then wandered off into negative territory.
And not only that, the business case now had to include sustainability and green benefits, many of which also were ‘intangible’.

So lots of sophisticated ‘guestimates’ and fudging with a nod and a wink became the norm and everyone jumped on the bandwagon, from senior management with MBA credentials to junior accountants; all began to succumb to the glib rhetoric, the blind leading the blind.

And this is in an era when the other sciences were going gang-busters- sending orbiters to Mars, decoding the genome, using stem cells to replace organs and AI to smarten the planet’s infrastructure. But of course it was still far too hard and inconvenient to nail the simple science behind quantifying indirect IT benefits.

So to bolster the myth further, the IT business case template was born- a very authoritative document. Just fill in the blanks and let the creative accountants do the rest.
‘What’s you’re best estimate of the benefits realizable from a Business Intelligence, Supply Chain, Marketing or HR system as well as all the other stuff needed to support it; like a new service-oriented architecture, broadband communications network, data warehouses, security software, cloud technology etc.

Well- just pick a number.

But by mid 2000s the fragile house of cards was starting to wobble. The effect of all this ultra-sloppy, lazy accounting was starting to ripple through the enterprise, ending up in the bottom line. Project prioritization, long term planning, essential infrastructure upgrades, all were being distorted- skewed towards projects with short term easy-to-compute benefits, but little else. But now the big-ticket projects, essential to cope with a new world of realtime transaction processing, online sales and automated supply and distribution wouldn’t wait.

Rigorous, realistic intangible benefits analysis is essential to confirm the payoff from these systems- process reengineering to re-energize the organization, improved customer service and pricing to maximize economic value, optimised decision support to leverage knowledge assets and smart infrastructure upgrades to minimize unforeseen disasters.

But on the other side of the universe the environmental and health industries had grasped the nettle thirty years previously and basically solved the problem.

What is the value of a new heart drug? It’s the percentage of lives saved or extended when compared with the old ‘legacy’ or non-existent heart drug. A 10% improvement in lives saved or extended can easily be translated into a tangible increase in productive working hours as well as reduced health care costs. So the reduction in the risk of heart patients dying early becomes the quantifiable benefit and any side effects becomes a cost.

Same with the environment. What are the benefits from the genetic engineering of crops or saving a wetland. If the new genes reduce the potential for disease, then the reduction of risk of crop losses becomes a calculable benefit. If they cause the spread of resistant weeds or insects or can’t handle droughts- then that’s a cost.
If remediating fish spawning wetlands reduces the risk of fish extinction then that’s quantifiable benefit. If it reduces the ability of developers to build more flood prone houses then that’s a public benefit too.

Now back to IT. You say that’s fine for industries like Healthcare and the Environment, where the risks and benefits are obvious. But you can’t translate that approach to trickier stuff like the impact of IT on customer service or management decision-making.

Yes you can!!

The smarter corporate strategists and operations research groups including this Centre have been developing and applying techniques for over twenty years that successfully challenge the ‘intangible benefits’ myth.

They have combined risk theory with decision theory, tweaked it with some additional AI and come up with better enterprise planning, value modeling, system prioritization, evaluation and audit, and service optimization on a continuous basis. The results- a much healthier, profitable and more resilient enterprise.

And this is only the beginning for the future of the dynamic smart business case.

In the 21st century it will be integrated with a host of other new and more science-based planning techniques- risk analysis, forecasting, Bayesian probability networks and AI-based process optimisation algorithms; as the enterprise of the future positions itself to be a largely autonomous entity able to better react, seek new opportunities and re-create itself in a fast-changing and uncertain world.

The smart business case of the future therefore should not be seen as a standalone tool, but as a dynamic and integral part of enterprise planning and modelling. Unless it is applied rigorously, it can distort the whole fabric of the organisation.

Projects and services and products don’t end abruptly. They get absorbed into the fabric of the enterprise as they interweave with other processes, often emerging as part of a new technology or service. The smart business case should therefore be an evolving process also, constantly adjusting to the evolving nature of the enterprise.

It’s therefore high time that the whole crumbling edifice of the mythology of intangible benefits was put to rest and the business case became a lot smarter.

After all- you can’t have a smart enterprise or a smart planet without support from a smart business case.

And it is the 21st century.

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The Intelligent Enterprise

2010-11-09T20:58:00.000-08:00

The enterprise of the future will increasingly depend on a wide range of rigorous artificial intelligence systems, algorithms and techniques to facilitate its operation at all levels of management.

As described in The Adaptable Enterprise blog, major decisions incorporating sophisticated levels of intelligent problem-solving will increasingly be applied autonomously and within real time constraints to achieve the level of adaptability required to survive in an ever changing and uncertain global environment. This trendline describes these techniques and their application.

A number of artificial techniques and algorithms are rapidly reaching maturity and will be an essential component of Intelligent Enterprise Architecture of the future including:

Genetic algorithms- solution discovery and optimisation modelled on the genetic operators of cross over, replication and mutation to explore generations of parameterised options.

Bayesian networks- graphical models representing multivariate probability networks; providing inference and learning based on cumulative evidence.

Fuzzy Logic- non-binary methods of decision-making -allowing information inputs to be weighted and an activation threshold established.

Swarm Intelligence- combining multiple components to achieve group intelligent behaviour.

Neural networks- pattern discrimination techniques modelled on neuron connection.

Expert Systems- rule based inference techniques targeted at specific problem areas.

Intelligent Agents- this form of AI is particularly relevant to the future enterprise architecture, because it is designed to be adaptive to the web's dynamic environment; that is, an agent is designed to learn by experience. They can also act collaboratively in societies, groups or swarms. Through swarming behaviour agents can achieve higher levels of intelligence capable of making increasingly complex decisions autonomously

The above techniques will continue to be enhanced and packaged in different combinations to provide immensely powerful problem solving capability over time. The technology is slowly being applied discretely within business intelligence, data mining and planning functions of enterprise systems.

However AI is yet to realize its full potential within the enterprise model by being applied to decision-making in a targeted autonomous fashion. When this happens over the next decade, the quality of decision-making and concommitant reduction in operational and amanagement risk is likely to be significantly improved.

Multi-Agent Cyber Ecosystems

2010-11-01T22:26:00.000-07:00

Cyber-Agents in the form of networked systems of small intelligent autonomous programs are playing an increasing role in managing business and lifestyle services. Their real-life counterparts include human social communities, social insect colonies, the brain, immune systems and the Web itself.

When such virtual agents interact with each other cooperatively they form a collaborative multiagent system, which greatly enhances their level of operational efficiency and problem-solving capacity. Such systems are already routinely applied in a wide range of complex operations including- marketplace trading, financial management, communications, social networks, scientific computing, manufacturing and power distribution.

Because of their potentially high level of collective intelligence, often far exceeding individual expert analyses, they will increasingly be applied to critical decision support functions across all aspects of our social, financial and engineered infrastructure in the future.

At a personal level, virtual agent and avatar programs also learn user’s interests preferences and habits and provide proactive personalised assistance to enhance individual and team productivity in the workplace; analogous to personal and executive assistant functions in the real world.

An ideal software agent should not only know its goal and determindly try to achieve it, but as with human agents, it must also be adaptive; capable of learning from experience and responding to unforeseen circumstances when required with a repertoire of knowledge and problem-solving techniques. It should also be relatively autonomous by sensing the current state of its cyber-environment and acting independently to make progress towards its objectives.

Collaborating agents also belong to the wider class of complex adaptive systems- CAS. Such systems are able to spontaneously coordinate their activities in much the same way as crowd-sourcing achieves; cooperatively applying knowledge from a diverse set of databases, algorithms and repositories across the Web. For example broker agents process requests against a domain ontology by finding other agents that can satisfy the required knowledge goals. Resource agents can then be recruited to support the implementation protocols required between user queries and information resources.

Such agent systems are already beginning to play an increasingly important role in enterprise applications such as e-commerce, marketing, scientific computing, intelligent manufacturing, home automation, component-based software construction and smart power grid management.

As multiagent technology moves into the marketplace there is also increasing interest in techniques for modelling collaborative systems as well as methods for constructing them. The most promising approach to multiagent design employs artificial intelligence techniques based on artificial life research. Virtual organisms are programmed to self-organise or adapt autonomously in response to their changing environmental demands, by applying evolutionary principles. Such AI mechanisms incorporate neural networks, evolutionary algorithms and other advanced learning techniques to allow realistic and flexible adaptation.

Basically, Agent-based modelling becomes essential when the difficulty of simulating large numbers of interactions is too complex for traditional analysis. Complex real-world behaviours reflecting millions of decision processes such as traffic congestion, epidemics and financial crashes will increasingly require the application of such techniques. There are already plans to create models with up to 10 billion agents, which could simulate the interactions of a large city population or even the entire planetary biosphere.

Rational agent programming is another related field, used for monitoring and managing complex control systems in engineering and communications, requiring the integration of multiple sub-systems and dynamic controller feedback loops. Automatically monitoring such complex environments is difficult because it is necessary to process large quantities of data acquired by different sensor systems.

Over time, artificial evolution will codify and combine the behaviours of the most effective agents to evolve fitter populations, with each succeeding population better adapted to serve the user's interests and goals. Such agent ecosystems will continually renew themselves as their environment changes, eventually having an enormous social, economic and political impact.

When multiple related multiagent systems link and interact they create powerful cyber-agent ecosystems with emergent properties.
New types of behaviour will start to emerge, not previously envisaged by designers and not capable of being reverse-engineered; interacting counter-intuitively with other critical enterprise operational and administrative processes on the Web, such as cyber-security protocols.

In the near future it is highly likely that such intelligent cyber-agent ecosystems will play a major role in the autonomous management and evolution of the future enterprise.

Cyber-Infrastructure for World 2.0

2010-11-01T22:25:00.000-07:00

Our future World 2.0 will face enormous challenges from now into the foreseeable future, including global warming, globalisation and social and business hyper-change.

Global Warming will create shortages of food and water and loss of critical ecosystems and species. It will require massive prioritisation and re-allocation of resources on a global scale.

Globalisation will require humans to live and work together cooperatively as one species on one planet- essential for our survival and finally eliminating the enormous destruction and loss of life that wars and conflict inevitably bring.

Social and usiness change will present myriad challenges relating to building and maintaining a cohesive social fabric to provide democracy and justice, adequate levels of health and education, solutions to urban expansion, crime prevention, transport congestion and food and water security, in a fast changing global environment. This will require adaptation on a vast scale.

It is apparent that in order to meet these challenges, humans must harness the enormous advances in computing and communications technologies to achieve a complete makeover of the world’s Cyber-Infrastructure.

The infrastructure of the new cyber reality now affects every aspect of our civilisation. In tomorrow’s globalised world a dense mesh of super-networks will be required to service society’s needs- the ability to conduct government, business, education, health, research and development at the highest quality standard.

This infrastructure will be co-joined with the intelligent Internet/web, but will require additional innovation to facilitate its operation; a transparent and adaptable heterogeneous network of networks, interoperable at all levels of society.

In the last two decades tremendous progress has been made in the application of high-performance and distributed computer systems including complex software to manage and apply super-clusters, large scale grids, computational clouds and sensor-driven self-organising mobile systems. This will continue unabated, making the goal of providing ubiquitous and efficient computing on a worldwide scale possible.

But there’s a long road ahead. It is still difficult to combine multiple disparate systems to perform a single distributed application. Each cluster, grid and cloud provides its own set of access protocols, programming interfaces, security mechanisms and middleware to facilitate access to its resources. Attempting to combine multiple homogeneous software and hardware configurations in a seamless heterogeneous distributed system is still largely beyond our capability.

At the same time tomorrow’s World 2.0 enabling infrastructure, must also be designed to cope with sustainability and security issues.
It is estimated that The ICT industry contributes 2-3% of total Greenhouse Gas emissions, growing 6% per year compounded. If this trend continues, total emissions could triple by 2020. The next generation cyber-architecture therefore needs to be more power-adaptive. Coupled with machine learning this could achieve savings of up to 70 % of total ICT Greenhouse emissions by 2020.

But the world is also grappling with the possibility of cyber-warfare as well as increasingly sophisticated criminal hacking, with an estimated 100 foreign intelligence organisations trying to break into US networks. A global protocol safeguarding cyber privacy rights between nations, combined with greater predictive warning of rogue attacks, is critically needed. The next generation of cyber-infrastructure will therefore have to incorporate autonomous intelligence and resilience in the face of both these challenges.

To meet these targets a lot will ride on future advances in the field of Self-Aware Networks- SANs. Previous blogs have emphasised the emergence of the networked enterprise as the next stage in advanced decision-making. SANs are a key evolutionary step on the path to this goal. Self-aware networks can be wired, wireless or peer-to-peer, allowing individual nodes to discover the presence of other nodes and links as required- largely autonomously. Packets of information can be forwarded to any node without traditional network routing tables, based on reinforcement learning and smart routing algorithms, resulting in reduced response times, traffic densities, noise and energy consumption.

Another major shift towards a networked world has been the rise of Social Networks. These have attracted billions of users for networking applications such as Facebook, LinkedIn, Twitter etc. These are providing the early social glue for World 2.0, offering pervasive connectivity by processing and sharing multi-media content. Together with smart portable devices, they cater to the user’s every desire, through hundreds of thousands of web applications covering all aspects of social experience– entertainment, lifestyle, finance, health, news, reference and utility management etc.

With increased user mobility, location sharing and a desire to always be connected, there is a growing trend towards personalized networks where body, home, urban and vehicle sensory inputs will be linked in densely connected meshes to intermediate specialised networks supporting healthcare, shopping, banking etc.

The explosion of social networked communities is triggering new interest in collaborative systems in general. Recent research in network science has made a significant contribution to a more profound understanding of collaborative behaviour in business ecosystems. As discussed in previous posts, networked ‘swarm’ behaviour can demonstrate an increase in collective intelligence. Such collective synergy in complex self-organising systems allows ‘smarter’ problem solving as well as greater decision agility. By linking together in strategic and operational networks, enterprises can therefore achieve superior performance than was previously possible.

The key characteristics of the smart business network of the future will be its ability to react rapidly to emerging opportunities or threats, by selecting and linking appropriate business processes. Such networks will be capable of quickly and opportunistically connecting and disconnecting relationship nodes, establishing business rules for participating members on the basis of risk and reward.
This ‘on the fly’ capacity to reconfigure operational rules, will be a crucial dynamic governing the success of tomorrow’s enterprise. CIOs must also learn to span the architectural boundaries between their own networked organisation and the increasingly complex social and economic networked ecosystems in which their organisations are embedded.

In fact the business community is now struggling to keep up with the continuous rate of innovation demanded by its users. Social network solutions have the potential to help meet this demand by shaping the design of future architectures to provide better ways to secure distributed systems.

So what is the future of this new collaborative, densely configured networked world? What we are witnessing is the inter-weaving of a vast number of evolving and increasingly autonomous networks, binding our civilisation in a web of computational nodes and relational connections, spanning personal to global interactions.

By 2050 the new World 2.0 cyber-infrastructure will link most individuals, enterprises and communities on the planet. Each will have a role to play in our networked future, as the cells of our brain do- but it will be a future in which the sum of the connected whole will also be an active player.

The Greening System

2010-06-25T15:34:00.000-07:00

The net energy impact of an enterprise’s products and services on the community far outweighs the benefits of any savings in its computer processing operations.

Saving energy in the 21st century’s computing ecosystem is a vital component in achieving the goal of a sustainable society and is currently being addressed within the context of numerous emerging technologies including- flexible cloud processing, low-energy mobile and sensor communications, outsourcing of services, infrastructure virtualisation, application integration, embedded electronics and low energy processor design.

But of far more significance is the potential role of information and computing technology in reducing carbon emissions in most of today’s service processes- whether relating to power generation, manufacturing, transport, service delivery etc.

This revolution, using the computer as the most effective green machine ever designed, is rapidly taking shape with the emergence of the ‘smarter planet’ mantra. This has already been adopted by every major systems and software provider including- IBM, Cisco, Google, SAP, Apple, Intel, Microsoft and Oracle and promises the optimisation of the planet’s infrastructure.
This will presage more efficient healthcare, education, communication, utility and government services, as well as higher quality industry outcomes in construction, mining, travel, engineering, agriculture etc, by applying the latest advances in artificial intelligence, design, materials, electronics, computing and control sciences.

As well as the enormous energy reduction payoffs of smarter infrastructure, the ‘smarter planet’ will manifest in a limitless number of areas including-

Simulation-based Engineering- solving previously intractable design problems and achieving significant cost and energy reductions by applying computer simulated models and prototypes for testing purposes:

Transportation Systems- managing major traffic flows and supply chains, which will demand increasingly complex integration and scheduling via multi-modal transport networks:

Developing Nations Environments - allowing the populations of these countries to join the developed networked knowledge world and gain leverage through the application of cheap sensors and low cost intelligent mobile devices to help solve complex environmental and resource allocation problems.

Such global energy reduction potential, gained by using the computer to generate overall outcome savings are indisputable and in fact totally dwarf the benefits gained from optimising computer processing as an end in itself.

But greater sustainability benefits are also conditional on the performance and effectiveness of computer processing, with real-time, event-driven applications becoming increasingly common. Computer processing energy gains must therefore evolve within the constraints of process performance needs. Higher performance processing may be more energy intensive, but still deliver far greater benefits in terms of outcome energy savings; so that deriving an optimum trade-off between energy input efficiency and performance output efficiency will be critical.

But an even more significant energy paradigm is emerging, which encompasses the capacity of the enterprise to deliver the sustainable benefits of its services to the wider community.

In the final analysis it is the enterprise that is the primary implementer of services to its customers- whether individuals or businesses. These are the beneficiaries or otherwise of its products and services.
A General Motors that keeps churning out gas-guzzling vehicles, totally unsuited to a greener environment and its customer’s needs, may do major harm to the planet no matter how efficient or sophisticated its computerised operational systems.

What this boils down to is the role of the future enterprise as the most relevant greening system in relation to the communities it services. It is the enterprise- small, large, public or private, which is the key enabling system to achieving a greener world.

Tomorrow’s enterprise will be the primary harnesser of human mind power, amplified by expanding computational intelligence in our world. Its potential therefore to create a greener future through its impact on the wellbeing of the wider community is what ultimately should be assessed as its true value to society.

A Future Brain Architecture

2010-04-30T16:04:00.000-07:00

Is today’s enterprise, including its IT acolytes, missing something very obvious and vitally important in its current management mindset or is it just an inability by a traditionally conservative constituency, to accept the radical paradigm shift involved?

Enterprise IT is beginning to dip its toe in the water and borrow some of its inspiration from biological models. For example, a number of the most valuable AI techniques routinely applied in business- genetic algorithms, neutral networks, DNA and swarm computation, are biologically based, as is the concept of the organisation as a complex ecosystem, rather than a rigid hierarchical structure, largely disconnected from its environment.

Networks are also getting a look-in. Complex decision-making, using elements of autonomous, self-organising and intelligent networks, incorporating complex feedback loops to monitor operational performance and enhance relationships with customers and suppliers, are now being trialled.

But the current enterprise management model is still missing the big picture- the shift towards an efficient, self-regulating, self-organising, self-evolving framework, so critical for survival in a future fast-moving, uncertain physical and social environment.
The most efficient blueprint for such an architecture and one honed over billions of years and governing all animal life, is the living brain; in particular the advanced human brain.

For the last thirty years, since the advent of computerised imaging techniques, scientists have been trying to prise open the secrets of the brain’s incredible power and flexibility. Not just how it computes so efficiently, but its ability to adapt, evolve and manage its 100 billion neurons and dozens of specialised structures, as well as all the relationships of the body’s incredibly rich cellular processes, organs and bio-systems. It has also mastered the capacity to flexibly adapt to a vast number of environmental challenges- both physical and social, while at the same time continuing to evolve and grow its intelligence at the individual, group and species level.

If only it was possible to harness this most complex object in the universe, to manage our own still-primitive, nascent organisational structures.

So what’s the secret to the brain’s incredible success in guiding the human race through its evolutionary odyssey? Well finally the creativity and perseverance of countless dedicated scientists is starting to pay dividends, with two recent major conceptual breakthroughs-
A Unified Theory of the Brain and the key to the Sub-conscious Brain.

Current theories of the mind and brain have primarily focussed on defining the mental behaviour of others using the brain’s mirror neurons. These are a set of specialized cells that fire when an animal observes an action performed by another. Therefore, the neurons ‘mirror’ or reflect the behaviour of the other, as though the observer was itself acting. Such neurons have been directly observed in primates and more recently humans and are believed to exist in other species, such as birds.

However despite an increasing understanding of the role of such mechanisms in shaping the evolution of the brain, current theories have failed to provide an overarching or unified framework, linking all mental and physical processes- until recently. A group of researchers from the University College London headed by neuroscientist Karl Friston, have now derived a mathematical framework that provides a credible basis for such a holistic theory.

This is based on Bayesian probability theory, which allows predictions to be made about the validity of a proposition or phenomenon based on the evidence available. Friston’s hypothesis builds on an existing theory known as the “Bayesian Brain”, which postulates the brain as a probability machine that constantly updates its predictions about its environment based on its perception, memory and computational capacity. In other words it is constantly learning about its place in the world by filtering input knowledge through a statistical assessment process.

The crucial element in play, is that these encoded probabilities are based on cumulative experience or evidence, which is updated whenever additional relevant data becomes available; such as visual information about an object’s location or behaviour. Friston’s theory is therefore based on the brain as an inferential agent, continuously refining and optimising its model of the past, present and future.

This can be seen as a generic process applied to all functions and protocols embedded in the brain; continually adapting the internal state of its myriad neural connections, as it learns from its experience. In the process it attempts to minimise the gap between its predictions and the actual state of the external environment on which its survival depends.

Minimising this gap or prediction error is crucial and can be measured in terms of the concept of ‘free energy’ used in thermodynamics and statistical mechanics. This is defined as the amount of useful work that can be extracted from a system such as an engine and is roughly equivalent to the difference between the total energy provided by the system and its waste energy or entropy. In this case the prediction error is equated to the free energy of the system, which must be minimised as far as practical if the organism is to continue to develop.

All functions of the brain have therefore evolved to reduce predictive errors to enhance the learning process. When the predictions are right, the brain is rewarded by being able to respond more efficiently and effectively, using less energy. If it is wrong, additional energy is required to find out why and formulate a better set of predictions.

The second breakthrough has come from a better understanding, again through neuro-imaging, of the brain’s subconscious processes. It’s been revealed that the brain is incredibly active, even when a person is not purposely thinking or acting, for example when daydreaming or asleep. It is in fact keeping subliminal watch, communicating, synchronising and prepping its networks for a conscious future action or response; continuously organising and refining its neural systems such as the cortex and memory; in the process using up to twenty times as much energy as the conscious mode of operation requires. This mechanism is called the brain’s default mode network or DMN and has only been recently recognised as a cogent system in its own right.

Now fast forward to the future enterprise, running under an architecture that incorporates these two knowledge breakthroughs. What are the additional benefits over the old model? Not too difficult to deduce.

Any organisation that is capable of constantly and seamlessly monitoring itself in relation to its internal functions and external environment; assessing its performance against its predictions and requirements in real-time through efficient feedback mechanisms; being aware of changes in its environment and opportunities to improve its performance and productivity; self-optimising its functions and goals; self-correcting its actions, searching autonomously for the best solutions for performing complex decision-making and constantly building on its experience and intelligence – must mark a vast improvement over the current model.

Not only that- this model has been tested and operationally proven in the cauldron of evolution over the past 5 billion years. Not a bad benchmark!
Too difficult to introduce into mainstream enterprise operations? I don’t think so, not in an era when we can build the world wide web, space-stations, large particle colliders, models of galaxies and the multiverse, apply genetic engineering techniques to solve diseases, grow new organs from stem cells and put a man on Mars!

Rebirthing Hal

2010-04-12T18:54:00.003-07:00

The arrival of super smart evolutionary computers, capable of autonomous reasoning, learning and emulating the human-like behaviour of the mythical HAL in Arthur C. Clarke’s Space Odyssey 2001 is imminent.

The Darwinian evolutionary paradigm has finally come of age in the era of super -computing. The AI evolutionary algorithm which now guides many problem solving and optimisation processes, is also being applied to the design of increasingly sophisticated computing systems. In a real sense, the evolutionary paradigm is guiding the design of evolutionary computing, which in turn will lead to the development of more powerful evolutionary algorithms. This process will inevitably lead to the generation of hyper-smart computing systems and therefore advanced knowledge; with each evolutionary computing advance catalysing the next in a fractal process.

Evolutionary design principles have been applied in all branches of science and technology for over a decade, including the development of advanced electronic hardware and software, now incorporated in personal computing devices and robotic controllers.
One of the first applications to use a standard genetic algorithm was the design of an electronic circuit which could discriminate between two tone signals or voices in a crowded room. This was achieved by using a Field Programmable Gateway Array or FPGA chip, on which a matrix of transistors or logic cells was reprogrammed on the fly in real time. Each new design configuration was varied or mutated and could then be immediately tested for its ability to achieve the desired output- discriminating between the two signal frequencies.

Such evolutionary-based technologies provide the potential to not only optimise the design of computers, but facilitate the evolution of self-organisational learning and replicating systems that design themselves. Eventually it will be possible to evolve truly intelligent machines that can learn on their own, without relying on pre-coded human expertise or knowledge.

In the late forties, John von Neumann conceptualised a self-replicating computer using a cellular automaton architecture of identical computing devices arranged in a chequerboard pattern, changing their states based on their nearest neighbour. One of the earliest examples was the Firefly machine with 54 cells controlled by circuits which evolved to flash on and off in unison.

The evolvable hardware that researchers created in the late 90’s and early this century was proof of principle of the potential ahead. For example, a group of Swiss researchers extended Von Neumann's dream by creating a self-repairing, self-duplicating version of a specialised computer. In this model, each processor cell or biomodule was programmed with an artificial chromosome, encapsulating all the information needed to function together as one computer and capable of exchanging information with other cells. As with each biological cell, only certain simulated genes were switched on to differentiate its function within the body.

A stunning example of the application of Darwinian principles to the mimicking of life was development of the CAM-Cellular Automata Machine Brain in 2000. It contained 40 million neurons, running on 72 linked FGPAs of 450 million autonomous cells. Also the first hyper-computer- HAL-4rw1 from Star Bridge Systems reached commercial production in 2000. Based on FPGA technology it operated at four times the speed of the world's fastest supercomputer.
And at the same time NASA began to create a new generation of small intelligent robots called ‘biomorphic’ explorers, designed to react to the environment in similar ways to living creatures on earth.

Another biological approach applied to achieve intelligent computing was the neural network model. Such networks simulate the firing patterns of neural cells in the brain, which accumulate incoming signals until a discharge threshold is reached, allowing information to be transmitted to the next layer of connected cells. However, such digital models cannot accurately capture the subtle firing patterns of real-life cells, which contain elements of both periodic and chaotic timing. However the latest simulations use analogue neuron circuits to capture the information encoded in these time-sensitive patterns and mimic real-life behaviour more accurately.
Neural networks and other forms of biological artificial intelligence are now being combined with evolutionary models, taking a major step towards the goal of artificial cognitive processing; allowing intelligent computing systems to learn on their own and become experts in any chosen field.

Eventually it will be possible to use evolutionary algorithms to design artificial brains, augmenting or supplanting biological human cognition. This is a win-win for humans. While the biological brain, with its tens of billions of neurons each connected to thousands of others, has assisted science to develop useful computational models, a deeper understanding of computation and artificial intelligence is also providing neuroscientists and philosophers with greater insights into the nature of the brain and its cognitive processes.

The future implications of the evolutionary design paradigm are therefore enormous. Universal computer prototypes capable of continuous learning are now reaching commercial production. Descendants of these systems will continue to evolve, simulating biological evolution through genetic mutation and optimisation, powered by quantum computing. They will soon create capabilities similar to those of HAL in Arthur Clarke's "Space Odyssey 2001"- and only a few decades later than predicted.

However the reincarnation of the legendary HAL may in fact be realised by a much more powerful phenomena incorporating all current computing and AI advances - the Intelligent World Wide Web. As previously discussed, this multidimensional network of networks, empowered by human and artificial intelligence and utilising unlimited computing and communication power, is well on the way to becoming a self-aware entity and the ultimate decision partner in our world.

Perhaps HAL is already alive and well.

Model-Based Development

2010-02-25T13:19:00.001-08:00

Software and system development needs to seriously grow up- and fast. It urgently needs to become far more rigorous and dependable if it’s to have any chance of meeting critical 21st century process engineering requirements. Model-Based Development- MBD might be the answer.

Two factors have conspired to transform it from adolescence to maturity.

Firstly, the rapidly increasing complexity of modern computer systems, applied more frequently in life critical contexts.
Secondly, the relentless pace of change driving process and system obsolescence.

The increasing complexity of modern computer software threatens to place an upper limit on our capacity to improve and optimise the primary processes governing our civilisation. Modern society is built around the delivery of precise real-time processes and services, which must increasingly meet critical benchmarks of efficiency, integrity, transparency and adaptability.

Even generic applications such as operating software, office management and resource planning systems etc, require hundreds of software engineers to develop and maintain them. But that degree of complexity ramps up exponentially for larger automated systems covering the range of enterprise, government and scientific applications- supply chains, production and process control, social and media software, communications, space, energy, engineering transport and disaster management services.

Complex software systems also need to constantly evolve to meet the latest shift in business and environmental pressures and practice. As a result, errors and poor quality performance built into early versions can quickly compound, with the system ending up in gridlock and malfunctioning.

Even worse, the problem is escalating as computer scientists and engineers push the boundaries of the possible; seeking to integrate diverse applications across multiple platforms, while at the same time implementing advanced solutions incorporating augmented reality, intelligent agents and location-based awareness. The problems of complexity and change can only get worse as solutions are required relating to the next generation of super systems managing global warming impacts, smart AI and sensor-embedded infrastructure and ecosystem evolution.

But there’s light at the end of the tunnel and it revolves around implementing Model-Based Development- MBD methods, incorporating mathematically verifiable design and testing.

MBD, as its name suggests, creates models of the required functions linked to a specific domain, which refers to a particular knowledge field, such as a manufacturing, telephone networks or weather monitoring. Software design therefore starts with high-level domain characteristics and properties, rather than a set of generic computing functions. In the MBD paradigm, the domain expert can review the model and point out missing functionality or essential links between elements within the system, without needing to understand sophisticated programming techniques.

The methodology depends primarily on the use of domain specific modelling languages that can be used to simulate and authenticate a system graphically before building it- as is common for current CAD systems. The use of such languages allows developers to create a formal model of the system, run it on a workstation and analyse its performance with automated tools. Finally code and test cases can be generated and automatically verified. Use of such tools in the software development lifecycle has the potential for substantial payoff, by avoiding many costly process malfunctions and reworking iterations.

Generic modelling languages such as UML are in wide use, but often result in large, complex models while Domain Modelling languages can incorporate relevant business rules and design concepts related specifically to the domain in a much more compact form.

Control system engineering and science provide the role model for this approach, based on formal logic and algorithms developed over many years. Although formal software and mathematical methods have been used for safety and security critical systems in applications such as nuclear power, chemical plants, space and defence they have not achieved widespread use in commercial or industrial software engineering. However this is likely to change as several key trends now begin to make this a more practical proposition.

First there is growing acceptance of model-based development for the design of embedded systems using toolsets such as MATLAB Simulink. This allows for rapid prototyping and design verification of test control and signal processing, particularly in avionics and electronic automotive systems.

Second is the growing power of formal verification tools, particularly model checkers. This software examines all possible combinations of input/output states and is therefore much more likely to find design errors than traditional testing.

The entire system is mapped and developers then can create the code and integrate it. Software development and maintenance is accelerated because the programmers have a clear idea of all the required functionality and how it relates to other elements in the system before coding.

Finally there is also much less risk of catastrophic programming errors, because engineers can detail the links between software elements beforehand, similar to CAD technology. If a component is missing or has been overlooked, it can be easily added to the model in a later step.

With the growing acceptance of MBD techniques, software development might finally have come of age.

Convergence of X-Reality

2010-01-07T15:58:00.000-08:00

First there was Virtual Reality-the creation of simulated games, objects and avatars; narratives embedded in online virtual worlds such as Second Life and World of Warcraft, with 15 million subscribers.

Then came Augmented Reality- created by integrating or mixing real objects and natural spaces with layers of related computer-generated data,images and designs; enabling real and virtual scenarios to be seamlessly combined. Basic forms of AR technology are already being used to gain a more immediate and accurate sense in practical applications such as engine repairs, wiring assembly, architectural design and remote surgery.

But now emerging from the evolution of cyberspace is Cross or X- Reality, with the boundaries between the real and the virtual extended yet again and becoming increasingly blurred in the process.

X- Reality environments essentially fuse two technologies- sensor networks and virtual worlds, bringing real world and realtime information into fully immersive virtual worlds and vice versa.

In hindsight it can be seen that Virtual and Augmented Realities are early phases in an ongoing evolutionary transition towards the acceptance of virtual forms as part of everyday human cognition. In the process we have crossed the threshold into a new space, extending human perception and interaction; linking ubiquitous sensory and actuator networks based on low cost microelectronic wireless technologies to create mixed realities.

The game is now on. By 2030, X-Reality will usher in an era of vastly extended reality indistinguishable from our present world, which has evolved over the period of life’s existence. In other words the world is evolving its own electronic nervous system via a dense mesh of sensory networks, eventually connecting and encompassing every object- living and non-living, on the planet. Such sensor networks help integrate physical reality into virtual computing platforms generating the ability to react to realworld events in automated fashion. This is creating a revolutionary relationship between human society and the Web, with the urgent need to understand the way our behaviour and future processes will become irreversibly shaped by cyberspace.

Cross reality environments can therefore serve as an essential bridge across sensor networks and Web based virtual worlds. The Web is already beginning to host an immersive 3D sensory environment that combines elements of social and virtual worlds with increasingly dense geographical mapping applications, allowing the monitoring and planning of natural and urban ecosystems- particularly its capacity to cope with climate change.

X-reality will be implemented according to the integration of key design technologies including-

Synchronously Shared Information- users will require open access to realtime data feeds and collection of information for analysis via centralised virtual command centres. Eventually control will devolve to decentralised self-organising and autonomous management systems working in partnership with users.

Complex Realtime Visualisation - users must be able to easily and flexibly visualise complex data, often delivered in 3D form. This will involve a high level of interactivity and collaboration, applying sensor-driven animation and the application of intelligent agents or avatars.

Ubiquitous Sensor Portals- such I/O devices designed for rich two-way cross-reality experiences, which can stream virtual and remote phenomenon into the user’s physical space; for example via video feeds and images uploaded from cameras. But this process can also extend into the past, allowing realtime access to historical data streams, vital for trendline analysis in business and the sciences.

Smart Phones- these will increasingly provide an intuitive interface that facilitates group collaboration in an ad hoc manner, via gesture as well as touch. Physical movement for outdoor users requires extreme mobility. Allowing augmented reality on smart phones that can query sensor networks and connect with shared online worlds paves the way for immersive mobile X-Reality.

Complex Event Processing- CEP- sensor networks will be particularly valuable in the future for generating data that tracks complex phenomenon in the real world, detectable by high-level pattern matching and logic inference techniques. Applications include- monitoring building and infrastructure maintenance, manufacturing and supply chain operations via RFIDs as well as environmental emergencies such as fire and pollution risks. In addition, CEP systems will help make sense of conflict zones, ecosystem health, field operative performance and traffic flows and events.

By 2030 most of our lives will be totally immersed in this shared reality. It will also redefine how we manage the vast and growing repository of digital information on the web- linking art, entertainment, work, science and daily life routines such as shopping, gaming and travel.

The Future Enterprise will be equally enmeshed- dependent on the management of its marketing, production and logistical operations and services via the medium of X-Reality.

Adaptive Business Intelligence

2010-01-03T14:57:00.000-08:00

The concept of adaptability is rapidly gaining in popularity in business. Adaptability has already been introduced into everything from automatic car transmissions to sentient search engines to running shoes capable of adapting to the preferences of each unique user over time, to business management.

Adaptive business intelligence is a new discipline which combines three components- prediction, adaptation and optimisation. It can be defined as the discipline of using prediction and optimisation techniques to create self-learning decision systems.

Managers work in a dynamic and ever-changing economic and social environment and therefore require constant decision support in two linked timeframes- what is the best decision to make now and how will this change in the future.

The general goal of most current business intelligence systems is to access data from a variety of sources, to transform it into information and knowledge via sophisticated analytic and statistical tools and provide a graphical interface to present the results in a user friendly way. However this doesn’t guarantee the right or best decision outcomes.

Today most business managers realise that a gap still exists between having the right information and making the right decision. Good decision-making also involves constantly improving future recommendations- adapting to changes in the marketplace and improving the quality of decision outcomes over time. This involves a shift towards predictive performance management- moving beyond simple metrics to a form of artificial intelligence based software analysis and learning such as evolutionary algorithms.

Future Trends

The future of business intelligence therefore lies in the development of systems that can autonomously and continuously improve decision-making within a changing business environment, rather than tools that just produce more detailed reports based on current static standards of quality and performance.

It must incorporate techniques that build autonomous learning, with feedback loops that generate prediction and optimisation scenarios to recommend high-quality decision outcomes; but also with an in-built capacity to continuously improve future recommendations.

The importance of such an evolutionary paradigm wil be esential in an increasingly competitive and complex business environment. It is regressive to continue to rely on software support systems that repeatedly produce sub-optimal demand forecasts, workflows or planning schedules.

The future of business intelligence lies in systems that can guide and deliver increasingly smart decisions in a volatile and uncertain environment.

Future Enterprise- Evolution of Cloud 2.0

2009-11-17T00:45:00.000-08:00

A major shift in IT business models will emerge from the next incarnation of Cloud Computing- Cloud 2.0.

The Cloud is a metaphor for shared infrastructure, software and data within the web. The original cloud concept emerged in the sixties, not long after the commercial genesis of computing, with the advent of the Service Bureau. This allowed smaller companies to share in the benefits of the golden computer age by running their applications on large service provider mainframes. Access was provided by punched card readers and later by remote computer terminals with printer output.

Minis and desktop personal computers then dominated during the seventies and eighties and the original service bureau industry faded away. In the nineties the Internet and Web evolved, allowing online remote services to return; this time based on the client-server model linked to in-house PCs.

Cloud computing is the next evolutionary step in shared computer processing, using virtualised information services delivered on demand over the Internet, circumscribed with SLAs and user–based pricing. The major advantages as in the sixties, are lower costs and fewer technical maintenance and upgrade problems. As with the original service bureaus, the computing infrastructure and much of the application software is based on reliable services delivered via remote data centers; this time accessible via a web browser.
In addition the Cloud is evolving to deliver personalised intelligent and mobile applications; for example streaming SaaS- Software as a Service using virtual assistants to organise data-mined information. AI empowered mobile applications might include sharing time-critical market information, planning meetings, responding to voice commands or analysing traffic patterns to determine the speediest or most fuel-efficient route for an individual.
Most of the major service and software providers such as IBM, EDS, Apple, Google, Amazon, Yahoo, Microsoft and e-Bay have now established significant and expanding cloud services, providing access to their proprietary databases through Web APIs. Cloud service categories now cover a large range of standard applications including-
Social – social networks, video and photo sites, virtual worlds, and multi player gaming
Business–office and workflow, customer relationship and sales, workforce, supply chain, financial and booking applications
Utilities- skype, paypal, peer-to-peer networking
Plus numerous statistical, user-generated, media, science, geographic and cultural services
But the next evolutionary phase of the Cloud will offer much more- in particular data linking. This will promote the sharing of datasets across diverse domains and between business, research and group partners, bringing the full semantic power of the Web into play and changing the face of business forever.

Different APIs rely on different ID and access mechanisms as well as data in specific formats. Therefore APIs have tended to slice the web into separate sources and silos, restricting its full potential.

Tim Berniers Lee’s recent publication of Linked Data Principles for connecting structured data on the web, provides a future blueprint for connecting information from different sources into a single global data repository; accessible by generic data browsers and standard database and query languages.

This not only allows web documents to be identified, but also real world entities using the RDF- Resource Description Framework schema and Web Ontology language- defining mappings between related domains. The web of linked data will therefore immeasurably expand the classic document web, creating a global data network capable of spanning and weaving multiple data sources.

An increasing number of data providers have now begun to implement these Linked Data principles, leading to the creation of an open global data space containing billions of links and coordinated by the World Wide Web Consortium.

Future Trends

The trendlines are now becoming clear. The web is advancing to a multi-dimensional medium for discovering, publishing and linking seamlessly documents and data, leveraging semantic intelligence and mobile capabilities.

Individual supplier services will obviously continue to build, but enterprises will increasingly access common data clouds as well as most utility services, which will in the longer term become open source or common global property.

Cloud spaces will continue to blend and split, fragmenting and reforming in unlimited combinations and permutations. They will share data as media organisations already do amongst themselves and with countless news aggregators such as Google. The divide lines between public and private ownership of application IP will also become fuzzy, with most applications and algorithms over time converting to generic forms- as many critical medical drugs now have.

Software and system suppliers will need to differentiate their products increasingly as focussed value-added services, targeted to specific enterprises and industries as IBM and others are currently doing in forging partnerships for their new customised Smart Planet infrastructure business models.

Service applications will therefore be differentiated primarily by the level of value they contribute to the enterprise. Enterprises in turn will become more strategically porous, linking their core processes and management decisions more organically with their partner service providers and the chameleon Cloud 2.0.

Network Science- Next Management Paradigm

2009-10-15T00:10:00.000-07:00

Network science will be a critical enabler of advanced enterprise management in the 21st century.

Major advances are already being made in applying the principles of network science to social, technological and business systems and it will be vital for the future enterprise to weave sophisticated network optimisation principles into all aspects of its operations.

Network science essentially involves analysing and managing the properties and dynamics of interconnected complex systems such as social groups, the Web, power grids, supply chains, markets, ecosystems and the brain.

Such networked systems are based largely on scale-free topologies. This is the natural architecture most relevant to the world around us and is modelled on structures with a relatively small number of hubs or nodes, each with a large number of connections and a much larger number of nodes with a relatively small number of links- broadly obeying a mathematical power law.

Knowledge of network topology and dynamics allows for optimisation and prediction of the behaviour of complex system processes and is becoming increasingly vital in managing major business activities, via information systems that control vast numbers of interlinked transactions, resources, agents and events.

Failure in a tightly coupled network such as a power grid or market system of a single node may force the failure of other nodes, resulting in cascades of failures, eventually triggering a catastrophic breakdown of the whole system, as in the recent collapse of the global economy.

Examples of potential applications of network science principles include-

Economies and Markets- reducing the risk of global failure by ensuring economic networks are more robust; by closely monitoring market signals and adjusting the topology of nodes and links to reduce the risk of runaway feedback loops and conflicts between local interests and global efficiency.

Ecologies and Biodiversity- improving the sustainability of ecological systems in a period of global warming with the capacity to provide timely warning of species and resource collapse. The network model provides a powerful representation of ecological interactions among species and highlights global ecological interdependencies, which can then be re-modelled to manage risk.

Business and Finance- improving the capacity to make quality decisions regarding markets and product development, to avoid the future collapse of companies such as General Motors and Lehman Brothers. In these instances, poor decision-making was amplified by the systemic risk of runaway cascading financial asset dependencies, due to overloaded coupling strengths between nodes and indeterminate feedback loops in the myriad interconnected customer and supply networks.

The relevance of network science for the future enterprise is therefore threefold-

Firstly, many of the systems involved in business may be modelled in the future by scale-free networks, such as supply chain, investment, infrastructure, production and customer systems; with nodes representing suppliers, assets, products, consumers and customer groups.

Secondly, an organisation’s systems may be modelled by networks, with nodes representing process and activity decisions and the links represented by the dynamic flows of information feeding them.

Thirdly, the architecture of the enterprise itself may be viewed as a network of control flows between decision-makers and operational agents. As processes become more complex and time critical they will be increasingly automated, but the architecture- the information and decision-making structures and channels, will still need to be continuously optimised.

Future Trends

As forecast in previous posts, the enterprise of the future will be driven by networked architectures- patterns of linked decision processes- constantly morphing, reforming and adapting to a continuous flux of a changing global environment.
Today’s traditional hierarchical or even flat management models will be incapable of supporting tomorrow’s vastly more complex and competitive techno-social environment.

Such techno-social systems composed of technological layers operating within the larger social and physical environment that drives process application and development will need a more integrated, adaptive and intelligent framework for achieving sound management capability, underpinned by network science.

Most real world transportation, manufacturing, computing and power infrastructure networks will be linked and monitored by sensors and tags embedded in largely autonomous networked societies; constantly adapting to global evolutionary dynamics.

Network science algorithms will be developed to monitor and engineer optimal decision topologies, critical thresholds and non-linear outcomes. These will combine with AI technologies to manage complex enterprise operational and management processes.

These algorithms will apply adaptive defence mechanisms, often providing counterintuitive approaches to the engineering and control of complex techno-social systems. Such techniques will be based on the manipulation of key nodes, links and pathways to induce intentional network behavioural changes- mitigating for example potentially catastrophic outcomes.

This will represent the new Network Science Management Paradigm of the 21st century.

The Networked Enterprise

2009-10-12T01:03:00.000-07:00

The enterprise of the future will be driven by a networked architecture- patterns of linked decision processes; constantly morphing, reforming and adapting to a continuously changing social and business environment.

The traditional hierarchical management model of the 20th century will be incapable of supporting the vastly more complex and competitive 21st paradigm of technological and social evolution.

Tomorrow's enterprise can be most effectively represented as a decision network model with decisions as nodes and information flows linking the relationships between them. This model represents an extremely powerful mechanism for understanding and optimising the adaptive enterprise of the 21st century- linked to but extending far beyond current simplistic process models.

Although process and object representations are a necessary and logical intermediary step in the evolution of enterprise system modeling and management, they fail to represent the underlying decision complexity of the real world and therefore fail to realise the true potential of a dynamic enterprise.

The core of the Networked Architecture will be the Decision Model, incorporating engineering methods based on decision pathways, with the capacity to dynamically route information and intelligence resources to critical decision-making agents in the enterprise.

This will not only involve the deployment of computing and information resources to adaptive decision nodes, but facilitate direct targeting of intelligence and problem solving capacity, enabling critical decision outcomes to be implemented in optimal time frames.

The latest 'Smart Planet' paradigm, in which the infrastructure and processes of the planet- whether manufacturing supply chains, electricity grids, water networks or traffic flows, are being re-engineered to optimise performance and achieve greener outcomes, will be the major driver for the networked enterprise of the future. The Smart Planet will demand that decisions be made more rigorously, efficiently, adaptively and therefore largely autonomously.

While SOAs focus on basic services, their capacity to implement complex decision processes is far from optimal. Current business intelligence and data warehouse software represents a halfway house towards this goal. But predictive techniques utilising AI will be the next stage, layered on current data mining and pattern recognition software and supported by a new generation of network-oriented database management systems.

Although the more far-sighted businesses are becoming aware of the need for such flexible small world network linkages, the support provided by today’s rigid organisational management architectures and philosophies has lagged well behind.

Tomorrow’s enterprise management must be far more pro-active and sentient in relation to environmental and structural change, avoiding being caught passively flat-footed in a bewildering flux of global evolution and competitive pressures.

Social Computing

2009-09-13T03:32:00.000-07:00

Social computing is the crucial next step following the rise of personal computing in the evolutionary computing stakes.

The term Social Computing has many connotations. It has come into prominence over the past few years due to the growth of dozens of hugely successful social networks such as Facebook and more recently Twitter; providing a global way of keeping in touch and exchanging information between friends and acquaintances.

In the process of linking with others, a network of social relationships is established. Social computing is in essence a way of codifying and exploring these relationships between people and agents in social spaces- crowds, communities, cities, markets etc.

Social computing applications focus on web-supported online communities such as social networks, wikis, blogs and virtual worlds, providing feedback on interactive social comment, entertainment, scientific and medical advances and business services. It also supports techniques for collective forecasting and decision-making, utilising the combined power of groups and communities to solve difficult problems such as those associated with major disasters and conflict. In addition it is increasingly applied to help analyse how changing technologies and policies affect political, social and cultural behaviour

A set of techniques and algorithms are now being developed based on network, cognitive and evolutionary theory, which will have major ramifications for the enterprise of the future. For example, business strategies and competitive markets have been increasingly characterised by turbulence, uncertainty and complexity. Consequently there is a need to model such markets and strategies as dynamic, evolutionary processes; that is, as complex adaptive systems.

In addition, data mining and simulation are applied to study social networks. Data mining can uncover patterns such as an organisation’s network structure, properties and relationships between suppliers and customers. Agent based social simulation or understanding social phenomena on the basis of models of autonomous agents has also grown tremendously in recent decades. Researchers use this approach to study a wide range of social and economic issues, including social beliefs and norms, resource allocation, traffic patterns, social cooperation, stock market dynamics and organisational decision-making.

There is as yet no effective widely accepted methods for modelling complex systems, especially those involving human behaviour and social organisations, but collaborative agent-based artificial life is currently the most promising approach.

Using agent-based modelling, an enterprise can construct a virtual competitive market that allows business strategists a way of investigating a range of realistic scenarios. For example agent models can account for interactions between irrational and rational investors in stock market bubbles.

The social network paradigm in turn has created the concept of the Social Fabric, which mediates the interactions of the network’s agents and information flows in much the same way as spacetime mediates particle interactions and exchanges. It therefore allows us to explore the dynamic social and cultural aspects of the world in which we live or in which an organisation exists.

Cultural Algorithms or CAs are one of several approaches to modelling the social fabric and the application of social intelligence to solve problems related to optimisation, based on particle or agent swarming. They are therefore a class of computational model derived from observing the cultural evolution process in nature. Embedding an activity or problem in a social fabric can improve its performance or solution outcome, enabling the system to find a better solution than the original over a number of population iterations.

Many organisations have adopted Web 2.0 tools to stimulate innovation and productivity. They are also beginning to embrace social networks as a way of more effectively marketing services and tracking customer behaviour.
But applying social computing to improve the quality of decision-making, process optimisation and prediction scenarios is not yet on the horizon for most.

In the future social computing will be an integral component of the strategic and operational management of the future enterprise, at the same time transforming the web into a truly collaborative and social platform.

Natural System Computing

2009-09-03T01:41:00.000-07:00

The author- David Hunter Tow, director of the Future Enterprise Research Centre- Australia, forecasts that the computing problems faced by the enterprises of the future will demand adoption of many of the same techniques evolved over the eons by organic natural systems.

Natural system computing is the field of research that investigates and applies computational techniques modelled on the information processing and self-organising operations of nature and organic systems.

The new paradigm of Systems Biology seeks to map the networks and information flows that govern the evolution and development of biological organisms using computational techniques. System biology interprets the functions and operations of life at the system rather than component level of genes, proteins, environmental factors etc. Natural processes can therefore be understood in terms of networked information input/output transformations including – gene regulatory, protein-to-protein interaction and biological transport networks.

Enterprises also can be represented by flows of information through networks, aimed at transforming input resources- knowledge and materials, into value-added outputs – products and services. This is achieved via process networks linking decision processes; increasingly mediated by interactive web services.

The problems faced in the future enterprise ecology are therefore no different from those of other organic systems and provide an opportunity to translate nature’s vast computing legacy to the potential field of organisational problem-solving and decision-making.

Present enterprise architectures such as SOA, are remarkably crude in comparison with those of biological organisms. But there is a time difference in their evolution. Life has been evolving for billions of years, while enterprise structures represent only hundreds of years of trial and error. By re-examining the natural processing of living organisms and adapting these evolved methods to benefit enterprise ecology, a huge leap forward in productivity can be achieved.

The biological sciences are already adopting this strategy by combining the methods and processes of natural systems with computer science in order to develop alternate green technologies, new materials, species conservation and disease prevention. Now it is the turn of business and management science.

Cellular automata, neural computing, and evolutionary algorithms are the most established artificial intelligence techniques born of natural computing, all of which have produced major payoffs for the enterprise. But recently a number of new biological forms have emerged including - swarm intelligence, artificial immune systems, bio-molecular computing and membrane computing, which promise additional significant advantages.

Swarm or collective intelligence is based on behaviour that emerges from the interaction of collections of individual agents such as bacteria, ants, termites, bees, fish or people. Swarm or particle optimisation algorithms, can be applied to find the solution to a problem, based on the best convergent behavioural fit between the individual agent and its group, according to a fitness criteria. This technique has already been successfully applied to learning, web, scheduling, planning and design applications.

Artificial Immune systems mimic the natural immune systems of organisms, which perform complex computations in a highly parallel distributed mode; using learning, memory and associative retrieval to solve recognition and classification problems. Applications include computer virus detection, fault diagnosis, pattern recognition, machine learning and control- all vital to the enterprise.

Membrane computing is based on the internal compartmentalised structure and functions of cells. Each membrane-controlled region contains objects that may be modified or transferred between regions according to a set of transformation rules. Applications include modelling photosynthesis and signalling pathways, quorum sensing in bacteria, computer graphics, encryption, sorting algorithms and solving computationally hard problems- many of which exist in the operational enterprise space.

Bio-molecular computing harnesses the power of complex molecules such as DNA and proteins. Data can be encoded in the form of bio-molecules and molecular biology tools used for operations such as cutting, splicing and control, which can be applied to perform arithmetic or logic operations on the molecular data substrate. This model is also capable of massive parallel computation, solving combinatorially difficult problems such as the travelling salesman problem or factoring a large number.

An even more radical molecular approach has been proposed using nucleic acid to emulate the mechanism of the original Turing machine in a cell, in the same way that DNA is ‘programmed’ to generate or ‘compute’ proteins. Individual cells could then be engineered to solve complex mathematical equations or diagnose illnesses

Sooner rather than later the future enterprise will have to come to terms with incorporating natural system computing within its standard toolkit. The enterprise of the future will need to deploy every problem-solving technique available to ensure its successful survival and propagation in the uncertain and chaotic environment of the 21st century.

The techniques already derived from natural systems-based computing have proved remarkably valuable. But this is just the beginning. The bar has already been set much higher. Advocates of the Smart Planet and Smart Enterprise paradigm are now demanding greater quality, reliability and performance from computing systems. This will involve a quantum leap in intelligent processing which in many cases only natural computing can deliver–real-time design optimisation, global resource allocation, emergency response, autonomous scheduling and maintenance and complex market analysis.

Harnessing the power of natural system computing is a logical next step for the future enterprise.

Complex Event processing- The Smart Enterprise

2009-08-04T04:11:00.000-07:00

Complex Event Processing or CEP, is a technology in transition- a precursor to more sophisticated autonomous decision processing emerging within the future enterprise.

CEP systems collect data from numerous sources and raw events within an environment such as a physical emergency situation or a company’s operations and uses algorithms and rules to determine in real time the interconnected trends and patterns that create complex scenarios. The results of this analysis is then channelled to the appropriate decision-maker for action.

This process should therefore be recognised as the beginning of the emergence of the ‘Smart Enterprise’. In time most major decisions within the enterprise will be associated with CEP events.

We are now also entering the era of the ‘Smart Planet' revolution. This is IBM’s mantra, but also that of Cisco, Google, Microsoft, Oracle, SAP, GE and every other major information services player. Adaptive and responsive techniques, largely autonomously managed, are beginning to be applied to the optimisation of the design, maintenance and operation of infrastructure and business processes. These include electricity and communication grids, healthcare, financial, transport, investment, building, engineering, emergency response and supply chain systems.

But in order for this revolution to occur, the enterprise must also evolve to be equivalently ‘smart’. Smart infrastructure without smart enterprise management won't compute.

Collecting the raw data for CEP will inevitably create information overload for the enterprise as sciences such as astronomy, biology and particle physics have already discovered, genrating massive datasets. Traditional relational databases and SOA architectures are not optimised for real time event processing, particularly as much of the data will be unstructured and garnered from heterogeneous sources such as web pages, videos, RSS feeds, market intelligence, statistical data, electronic devices and instrumentation, control systems and sensors.

In addition, CEP overlaps with the business intelligence domain. The latest CEP toolsets allow users to apply high level modelling tools, AI and query languages that allow them to implement business logic while processing event streams.

However, no matter how much filtering, pattern matching and analytic processing is applied to CEP data, human decision-making will still be a significant bottleneck. The future smart enterprise must have the flexibility to focus and deploy its cooperative intelligence in real time and autonomously, at all levels of the organisation in response to opportunities and competitive pressures in the marketplace.

The level of complexity of CEP and decision-making will continually and rapidly increase over time in response to the changing social and technological environment. The resulting complexity of networks of interactions involving customers, supply chains, services, markets and logistics will make it impossible for humans to respond effectively. It will become just too complex and time-consuming even for dedicated teams of humans to manage.

Real-time integration of disparate data and applications is a key challenge facing the future enterprise. Conventional approaches such as building a data warehouse to consolidate all data sources are expensive, slow and highly intrusive. A number of innovative CEP platforms are being developed in this sector based on enterprise information streaming models. These provide a virtual unified view of the data stream without first transferring it to a central repository.

The future smart enterprise will also be required to be proactive rather than reactive in order to optimise its response to a fast changing environment. That is it will be required to actively search for solutions and be knowledge driven. This will place further pressure on the need for real-time quality decision-making.

In the near future humans will be partners in the decision processes powered by CEP and smart algorithms, but over time their input, as for airline pilots and fast train drivers, will be largely symbolic.

Evolutionary Systems Development

2009-04-23T20:46:00.000-07:00

There has been a dramatic recent shift in sentiment in relation to choice of the best model for developing software systems. The shift has marked a change from the tradition of preparing a detailed requirements specification as the first phase in the development cycle, to a less rigid adaptive evolutionary approach.

The ongoing goal of software engineering is to ensure that a system meets its primary aims in terms of the quality criteria of functionality, flexibility, performance and reliability. Achieving rigorous standards of performance and reliability has never been the problem for developers; rather it has been their inability to capture a rigorous set of user requirements capable of delivering long lasting optimal outcomes.

This is similar to the problem of using rigorous deductive logic to draw conclusions from a set of axioms, but reaching a wrong conclusion because the axioms themselves are incorrect or incomplete.

Time and again this Archilles heel of software development emerges- particularly when a project is large, complex and operates within a dynamic environment. Systemic failure is more often the norm and the litany of collapsed projects keeps growing; particularly in the domains of government and business demanding planning and delivery of complex customer services such as health, education, infrastructure and communications.

A vast literature has accumulated on this endemic problem: how best to capture the enduring requirements of a system. It is the elephant in the room at almost every CIO seminar and conference.

A number of techniques have been applied over the past fifty years, each hopeful of delivering the magic silver bullet including- functional, data, entity relationship, process and object-oriented analysis applied at various levels of sophistication. Each manages to capture a particular facet or dimension of user aspirations- but never the whole set.

Libraries of tools and methods also cover all phases of the traditional software development cycle- requirements analysis, design, coding, testing, implementation, as well as project and quality management- but still the problem remains.

Organisations attempt to deal with the problem in a number of ways.
First by buying off-the-shelf, pre-packaged software, hopefully flexible enough to be easily tailored and adapted to an organisation’s requirements. But this solution only works if a reasonable functional match exists in the first place and if the level of built-in flexibility is sufficient to avoid costly re-working over time, beyond ad hoc version updates.

The second way is by linking together multiple functional components like a Lego set. But this also only works if the components are available and can be adapted independently by the customer and if they fit together without the need for complex middleware.

In past decades, these approaches often worked adequately for standard systems such as accounting, inventory, sales, maintenance, CAD, HR, job scheduling, project control, office systems etc. But even these became obsolete or unmanageable over time as protocols changed, customer expectations increased, technological change accelerated and the enterprise’s products and services evolved.

Perhaps in our efforts to tame the elephant, we have focussed on the wrong problem.

In the 21st century we live in a vastly different world of web services and SOA’s, cloud and mobile computing and enterprises which must continually adapt to a bewildering mix of competitive and economic pressures, almost on a daily basis.

On the other hand we have proof that immensely complex systems can be built and remain viable and continue to deliver real value over time- vast communication systems such as the Internet and World Wide Web, reliable operating systems such as Unix, Linux and Symbian, social networks such as Facebook and Myspace, families of powerful scripting languages based on Java, ever-improving search engines such as Google and Safari, easy to use databases such as SQL and an increasing number of flexible online e-business applications from the new utilities such as Amazon.

These are cooperative innovative works in progress, which have been tested through many iterations by scenarios and prototypes, before emerging in beta form; all developed in close consultation between developers and their user communities. And they continue to adapt as community needs evolve on a daily basis.

These are examples of the new emerging class of evolutionary adaptable systems.

The major driver for the emergence of this radical evolutionary paradigm is the accelerating rate of social, technological and economic change, particularly over the past twenty years. In almost all cases this acceleration will mean that long lead times for systems development are now untenable and almost certain to lead to obsolescence or outright failure- certainly before an adequate ROI is achieved.

It is becoming rapidly recognised that any realistic requirements engineering methodology must incorporate an evolutionary approach, combined with an efficient mechanism such as Agile programming and design techniques, for converting evolving functional and process requirements incrementally to a useable system. This enables the enterprise to adapt to the continuing dynamics of social, business and technological change, by continuously spawning new functions or incremental amendments, without disrupting its core processes.

The same change imperative applies to small as well as large systems. The risks inherent in smaller systems in the past just haven’t been as obvious or critical. In fact any significant system build that hopes to meet its user aspirations of long-term support and value contribution, must adopt an evolutionary approach.

Risks in evolutionary development also exist as for traditional systems - the risk that managers misread the environmental signals, such as in the case of GM’s disastrous planning decisions and continue supporting ineffective reporting systems; or that the updates and changes become so pervasive that the system becomes unwieldy and opaque, as in Microsoft’s early Vista system.

But the risk impacts from not following the evolutionary canon are far greater. Wrong management decisions can be quickly turned around by agile methods, if recognised in time. Building individual inappropriate functions can waste resources and cause annoying disruption, but don’t cause catastrophic project collapse and massive system re-design time delays and budget overruns.

In the future, the trend towards applying evolutionary techniques to software development will become embedded in IT best practice, particularly as this will be coupled with the parallel trend towards autonomic management of enterprises; interacting with the human and physical world on a real-time basis.

The record of systems development to date is appalling, but not through lack of the enormous level of innovation, effort and professional skills applied. It is because we have found it difficult to come to terms with a constantly evolving world impacting our built environment. We have ignored the fundamental principle that systems must continually adapt to changing environments if they are to survive.

This is as good a silver bullet as the IT industry is likely to get.
Evolution has been the universal driver of all systems- biological, social and now economic and computing, since the universe began and we ignore its wisdom at our peril.

Autonomic Computing Systems

2008-09-19T01:43:00.000-07:00

To cope with the ever-increasing complexity of managing distributed heterogeneous computing systems, there is a strong imperative for such systems to manage their own operations in relation to higher-level planning objectives. Such self-management is a hallmark of autonomic computing systems.

Objectives are represented by utility functions that users can specify in terms of clearly defined operational metrics. Utility functions are attractive for achieving autonomic computing because they provide a basis for converting higher-level performance metrics to lower-level system control parameters.

Web based services such as online banking and shopping are hosted on distributed computing systems comprising heterogeneous and networked servers. They ideally therefore should be self-managed. To operate such systems efficiently while satisfying stringent QoS requirements, multiple performance related parameters must be selected, capable of adapting to changing operating conditions.

For example, the computational workload a system must process might vary over time, with the possibility that hardware or software resources could fail or need replacing during system operation. For service providers to cope with the growing scale, complexity and time constraints of applications, supporting systems must become largely autonomic- that is capable of managing their own operation with minimum human intervention.

Current work on self-managing systems generally aims to re-engineer human expert domain knowledge in the form of automated rules, applying artificial intelligence techniques to achieve real-time performance optimisation.

Key management tasks that can be automated in computing systems include- power management, load balancing, configuration and dynamic resource provisioning. Properties common to all autonomic systems will therefore cover- self-configuration, self-healing, self-optimisation and self-protection.

An autonomic data centre of the future might host several applications or services on behalf of multiple customers. A customer might subscribe to a new service and specify preferred levels of performance and security defined in an SLA. The Centre will determine which resources are needed to satisfy the SLA, automatically provision and configure the appropriate server, database, storage and networking resources in accordance with those criteria and install and configure domain-specific software governing those resources. The Centre will then monitor and manage itself- sensing and safeguarding against faults, bottlenecks and attacks.

Future trends

Autonomic computing is quickly becoming an accepted practice and will dominate all scales of real-time computing in the future – from the Internet’s global operation, to Grid and Cloud computer networks to PC clusters.
This capability will eventually combine with autonomic software management to achieve complete automatic system management.
Autonomic computing will also be an important step on the path to the Adaptable Enterprise, which will require new web service building and maintenance capacity to be fast and seamless, taking advantage of market evolution and new opportunities.

Enterprise Risk Management

2008-06-25T04:20:00.000-07:00

In addition to standard risk management processes such as credit, foreign exchange and market risk, CFOs are increasingly being asked to report on ways to minimize risk in a wide variety of other areas including- new product investments, acquisitions, HR, brands, market share, customer acceptance and satisfaction and product liability- in fact all operational and strategic processes across the enterprise. This is causing a rethink on the systems required to monitor such broad-based risks.

A string of corporate disasters such as Enron, Worldcom, Ford and GM has transformed the status of risk management to a central role. Up until five years ago risk management was handled by actuaries and internal auditors. Now the discipline incorporates high-level corporate risk officers reporting directly either to the CEO or a board level risk management committee.

This trend is encapsulated by the emerging discipline of ERM- ‘enterprise risk management’- a set of broad-based management processes that integrates all risk areas from- insurance financial derivatives, foreign exchange and trade to credit, market, political, brand and image risks, to corporate governance, regulatory and legal issues and now escalating environmental impacts.

ERM aims to achieve systematic reduction in all risk outcomes facing enterprises today, but with the goal of creating balance between realizing opportunities and minimizing adverse impacts.

The quality of an organisation’s information technology architecture plays a pivotal role in this area both by both processing and integrating the data needed to manage risk and by ensuring that the company’s risk policy and planning is rigorously implemented.

However to date, the challenges in managing risk on an enterprise wide basis have proved daunting. According to a recent Deloitte Global Risk Management Survey, less than a quarter of businesses surveyed were able to integrate risk across any of the major functional dimensions- risk type, business unit or geographical area.

There is also the tendency for financial and manufacturing institutions to focus primarily on areas of traditional risk where data analysis and statistical techniques have been developed. This is a natural response, but in an environment where new and potentially lethal risks can emerge suddenly triggered for example by major catastrophes such as bird flu or a surge in the oil price impacting large vehicle sales, as in the case of Ford, it is important to look at the bigger picture and seek to anticipate and avoid those submerged risks that can abruptly sink even the largest enterprise.

Future Trends

Managing such potential risks in the future enterprise environment will require new architectures and information system safeguards to constantly and automatically monitor the quality of decision outcomes; in other words implementing a sophisticated Deep Decision Risk Audit Framework.

Even when when risk and audit plans are implemented, the associated critical decision processes that need to be activated, are either not integrated into the control architecture or the outcomes are ignored because of lack of substantive risk cost estimates.

Risk outcomes are inevitably associated with all decision-making processes and the quality of those decisions largely determine the survival and productivity of the enterprise. A deeper decision assessment process ensures that the risks associated with critical decisions, including those governing potentially catastrophic outcomes, are better anticipated, highlighted and quantified. This allows intervention before, not after critical thresholds are breached and before serious and chaotic escalation occurs as with the recent global financial meltdown.

Smart Business Networks

2008-06-24T03:04:00.000-07:00

Recent research in network science triggered by the boom in social networks on the internet, has made a significant contribution to a more profound understanding of networked behaviour in business ecosystems.

Networked ‘swarm’ behaviour for example can demonstrate an increase in collective intelligence. Such collective behaviour in complex self-organising systems exhibits impressive ‘smarter’ problem solving capability as well as greater agility.

By linking together in strategic and operational networks, enterprises are therefore able to gain synergy and achieve superior operational performance than was previously achievable using more rigid organisational models. By combining skills and resources as part of a larger networked entity, they have the potential to become much more agile and flexible, reacting to and taking advantage of emerging opportunities.

For example, In less than 10 years Amazon has moved from book retailing to become the world’s leading e-tailer, offering a complete business platform to support a networked model for traditional retailers including- facilitating searching by buyers and sellers, helping set pricing, managing logistical processes, settling payments, arranging fund transfers and authenticating the quality of goods and the credibility of buyers and sellers.

Thousands of retailers join the Amazon network every month to benefit from Amazon's accumulated experience and information relating to retail goods, buyers and sellers. Such new platforms demonstrate strong network characteristics- for example, the more users in the network, the more useful it becomes for all players and the more difficult it becomes for members to switch to another network.

Organisations are increasingly moving from today’s relatively slow and hierarchical model of a self-contained business based on a small number of closely coupled partners, to an open digital platform where business is conducted across rapidly created networks. This open model allows opportunistic linking to a wider range of global partners, connecting to different processes and information systems. The disadvantage of not moving to such loosely-coupled networks includes the inability to provide support for more complex, bundled and rapidly delivered products and services in a fast changing and demanding business environment

Future Trends

The key characteristics of the smart business network of the future will be its ability to rapidly react by picking, plugging and playing business processes and partners, reconfiguring rapidly to meet specific objectives.

In addition such networks will need to quickly and opportunistically connect and disconnect relationships, at the same time establishing business rules and operational logic for participating members on the basis of risk and reward. This ‘on the fly’ capacity to reconfigure new decision rules governing decision and operational processes, will be a crucial dynamic governing the success of tomorrow’s future enterprise.

Future enterprise IT managers must also learn to span the architectural boundaries between their own networked organisation and the increasingly complex social, financial and economic networked environments in which their organisations are embedded and must operate.

Decision Re-engineering

2008-06-12T05:59:00.000-07:00

Decision Re-engineering

The emerging Decision Engineering Architecture of the 21st century represents a major shift in managing the operations of the future enterprise. By optimising the decision networks of an organisation, greatly improved levels of efficiency, adaptivity and quality of decision-making can be achieved beyond current

Process and service oriented architectures. Combined with the new organisational model of intelligent and flexible knowledge processing, decision-engineering techniques are well positioned to accelerate major gains in the performance and productivity of the future enterprise.

Scientists are striving to more fully understand the ways in which network components interact with one another to influence complex processes. Thus attention has turned to the analysis of networks that operate at many levels.

The network model is well established in the social, biological and environmental sciences providing a much more realistic and powerful model of real world interactions.

This evolutionary model is now beginning to permeate the business and technology environments offering the next major step in their evolution.

The core of this framework in relation to the future adaptable enterprise is the capacity to dynamically route information and intelligence resources to critical decision nodes in the enterprise.

The most basic feature of any network is its architecture or topology, which places boundaries on how it acts and how it might have been formed. Network models are underpinned by Graph Theory, which defines the rules of network topology.

Future Trends

The classes of networks that exhibit different global features will be examined in future updates, to determine the architecture that will best fit the evolution of the adaptive enterprise together with techniques available within the emerging class of Decision Engineering Methodologies- to map, optimise and apply decision networks within an enterprise environment.

Applied to the new enterprise model of intelligent and flexible knowledge processing, decision engineering techniques are well positioned to accelerate major gains in the performance and productivity of the future enterprise

Re-engineering the decision processes of an organisation will be essential to the future unlocking of potential value and reducing operational and planning risk.

The Virtual Metaverse

2008-06-12T04:54:00.000-07:00

The Web is now beginning to host an immersive 3D sensory environment that combines elements of social and virtual worlds such as Second Life with increasingly dense geographical mapping applications using Google Earth and Microsoft's Virtual Earth.

Representations of the earth’s natural and urban environments can create increasingly detailed mirror worlds or models of reality. Combined with virtual worlds, they represent an emerging Metaverse of realised virtual earths, projected to have an increasing impact on business and society.

Such meshing of the virtual and real is already standard within Second Life, using repesentations of the social realities with which humans can interact- for example- a virtual Planetarium, a walk-in weather map and an International Spaceflight Museum where visitors can fly alongside life-size NASA rockets- all driven by real world data streaming technologies.

Users can routinely create avatars that hover and zoom over a world with computer generated human clones and scenery, social facilities, urban infrastructure and services, including shopping with real convertible currency.
Such virtual earths are also becoming increasingly common in consumer computer games utilising the enormous power of today's inexpensive graphic processors.

Future Trends

This increasingly immersive Virtual Metaverse will soon be accessible in 3D hologram form and on mobile media and will also be capable of integrating with augmented reality- a third stream of enhanced reality.

Augmented reality is created by integrating or mixing real objects and natural spaces with layers of associated computer-generated images and designs; so that in the future real, virtual and augmented scenarios can be seamlessly combined.

An architect for example could visualise the impact of a virtual building overlaid on a real landscape or perform a virtual tour around a planned or existing building with a client. Auto designers will see how small changes would affect an existing car body design or surgeons the effects of cosmetic surgery on a real patient. At a simpler level, coffee or pizza shop fronts captured in the Google Street application are already being linked to videos and images of the shop's internal experience as well as Google maps.

Such integrated real, virtual and mirror worlds will offer a major creative challenge for the future enterprise- providing the opportunity to amplify its tactical and strategic competitive reach, better manage global resources through logistical virtual simulations and explore novel and radical marketing scenarios. At this point in time business has been slow to realise this potential as marketing and planning departments play catchup with the more mature consumer virtual experience.

Eventually there will be many different second earths in our future- within an expanding virtual Metaverse. Businesses are already utilising virtual worlds for simulation and promotional purposes and in the future will develop and control their own cyber worlds. Over time these will overlap, merge, spawn and evolve into a seamless surreal metaverse with unlimited potential for both the enterprise and humanity.