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.
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.