All meaningful analytical activities should lead to action. The various modes of analysis used by many businesses are currently mostly disjoint. Visual analytics, which is very much in vogue, is almost totally disjoint, and insights are often difficult to implement in a production environment. Ideally such insights should interface to the analytical models being used in an organization, after management consideration. In a sales situation for example, anomalies might be investigated using various data mining techniques to discover why they exist. Remedial action would then be possible by creating new predictive models and optimizing the deployment of resources.
In any viable system, action is only possible through integration. It is inconceivable that any entity, either animate or inanimate, could pursue meaningful action if its various parts were not integrated through meaningful communication. And so the insights of management need to be communicated to operations in a manner that is meaningful to both.
Business intelligence, data discovery and visualization, and data exploration are effectively the eyes and ears of the organization. Predictive models are the conditioned reflexes, which may or may not be appropriate in the current business environment, and for this reason need regular re-programming – or refreshes. This can only happen if lines of communication between the various departments in an organization are efficient, and a common language spoken. So in the example of sales anomalies, managers would talk with analysts to communicate the problem, and analysts could then set about establishing why the anomalies exist. Once the reasons become clear then some level of change in operational activities will be implied – unless the anomalies are accepted as inevitable.
Changes in business operations usually imply changes to business processes and business rules. Integration between these two aspects has recently moved forward with the development of Decision Model and Notation (DMN), an executable notation that injects decision logic into business processes. Change also implies a redeployment of resources in some way, and it is here that optimization techniques play a crucial role. In the sales anomaly example, it might mean pulling resources away from unprofitable customer segments and redeploying elsewhere. This type of resource allocation is enhanced considerably through the use of optimization, and in some real-world scenarios, such optimization might be pseudo-realtime as conditions change, and problems and opportunities arise.
The state of play today is that optimization and predictive modeling is integrated to some degree. Optimization can be used to determine which predictive models and business rules should be used in particular circumstances. Business processes can be integrated with business rules through DMN, which in turn can be used with business process modeling (BPM) methods and technologies. Integration between the analytical modeling environment and production applications can also be achieved using standards such as PMML (although it does have limitations), libraries of predictive models and APIs. In reality however development and deployment of predictive models is also often disjoint – the models being reprogrammed in Java, or whichever language is used. The area where there is virtually no integration is between business intelligence (in all its forms), and model building. This situation is likely to get worse before it gets better, since there is a common misconception that visual analytics is a safe way to create business rules (making changes based on visual analytics implies some change to the rules driving operational activity). Until visual analytics is positioned firmly as a diagnostic tools and not a remedial one, then there will be little motivation to form a link between these two modes of analytical activity.
There is a long way to go in all of this, but it did take 50 years to automate business transactional activity and its management, and we are still in the early stages of understanding the avalanche of technologies which will inevitably automate many of our business decision processes.