Organizations often complain that they are drowning in data but starving for information. What they are seeking is insight and foresight from the treasure chest of raw and transactional data they already have combined with other information widely available. Business intelligence (BI) software tools have been presumed as the solution; however there is confusion because there is an emerging term, business analytics (BA), that is also heralded as the solution. What is the difference? And where does predictive analytics fit in?
In my part-time role as the Executive in Residence for the Institute of Management Accountants, I am compelled to remove the confusion.
How are BI and BA different?
To clarify, business intelligence consumes stored information. Business analytics produces new information. Examples are regression, segmentation, correlation, and clustering analysis. Predictive analytics, a subset of business analytics, leverages data within an organizational function and externally, that relies on workforce skills and competencies to drive better and faster decisions to improve their organization’s performance.
Queries and drill downs using business intelligence tools simply answer basic questions. Business analytics creates questions. Further, business analytics then stimulate more questions, more complex questions, and more interesting questions. More importantly, business analytics also has the power to answer the questions. Finally predictive analytics can display the possibility (and ideally the probability) of outcomes based on the assumptions of variables.
Here is a useful way to differentiate business intelligence, business analytics, and predictive analytics. Business analytics simplify data to amplify its value. The power of business analytics is to turn huge volumes of data into a much smaller amount of information and insight. Business intelligence mainly summarizes historical data typically in table reports and graphs as a means for those queries and drill downs. But reports do not simplify data nor amplify its value. They simply package up the data so it can be consumed. Predictive analytics reduce uncertainty to support decisions. Business analytics provide insight, and predictive analytics provide foresight.
Where do decisions fit in?
In contrast to business intelligence, business analytics, and predictive analytics, decisions provide context for what to investigate and analyze. Decisions only affect the future. A good rule is to work backwards with the end decision in mind. Identify the decisions that matter most to your organization and model what leads to making those decisions. By understanding the type of decision needed, then the type of analysis and its required source data can be defined.
Much is being written today about Big Data. Big Data can be defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, validate, storage, search, share, analyze and visualization. What is needed is to shift the discussion from Big Data to Big Value. Business analytics and its amplifier, predictive analytics, serve as a means to an end – and that end is faster, smarter decisions.
There is always risk when decisions are made based on intuition, gut feel, flawed and misleading data, or politics. If a management team is analytics-impaired, then its organization is at risk. Business analytics is the next wave for organizations to successfully compete and not only to predict outcomes but to reach higher to optimize the use of their resources, assets and trading partners among other things. It may be that the ultimate sustainable business strategy is to foster analytical competency and eventually mastery among an organization’s work force.
Gary Cokins is the author of several books. He is the coauthor of the new book Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance along with Lawrence Maisel.