Key Strategies for Profitable Business Analytics
Business analytics serve only one purpose – that of helping people make better decisions. These decisions might occur at the level of transactions, tactical operations, and strategy. Business intelligence for example is largely concerned with analysis of prior performance and support for diagnostics. It is mainly used to support tactical decision making by managers at various levels in the business, and is usually not useful for transaction based decisions or strategic decisions. These latter generally tend to consider macro factors such as economic conditions, competitor activity, market trends and so on.
The business analytics space is becoming quite crowded, with machine learning, prescriptive analytics and artificial intelligence adding to the analytic mix. Machine learning concerns itself mainly with applying algorithms to historical data, in an attempt to detect patterns of behavior that might be useful in future activities. In loan approval for example, we interrogate historical data looking for characteristics that might indicate a loan applicant will have no problem repaying a loan. Many loan approvals are now processed automatically with very little human involvement. Clearly there is considerable scope here for adding intelligence to operational applications dealing with customers, suppliers, employees and trading partners. As such the intelligence needs to be embedded into these applications so they are available at the point of work. This is also true of business intelligence, and particularly the embedding of various visual artifacts (charts, graphs, dials etc) into the applications that are used day after day in a production setting.
The current fascination with all things visual is understandable, but a business will not realize the efficiencies that business intelligence and machine learning can deliver, until analysis is embedded into production applications. Such analysis can speed up processing of transactional activity, and ultimately will completely automate a good deal of it.
Prescriptive analytics is fundamentally different from BI and machine learning in that it establishes how processes should execute to make best use of resources. BI and machine learning are concerned with what has happened or will happen. Prescriptive analytics is concerned with how to best use resources given various forecasts and plans.
