Business Intelligence versus Business Analytics

It would appear there is a shift in interest.  Business Intelligence is yesterday, Business Analytics is today.  The boundary between the two domains is however not that well defined.  Even Google Scholar turns up limited results for 2013/2014 and some academic articles make no distinction between the two terms.

Business Intelligence, as I see it, can be defined as the process of transforming signals/data into knowledge (or wisdom/enlightenment depending on your viewpoint).  So what is the definition of Business Analytics?  I was curious so collected a few interesting perspectives from various public sources which are catalogued below.

I actually quite like the definition in point #3 below from SAS Senior Vice President and CMO.

Business Intelligence = reactive/historical

Business Analytics = predictive/forecasting

#1 “In method, BA is an offshoot of BI. BA focuses on using data to net new insights, whereas traditional BI used a consistent, repeating set of metrics to steer future business strategies based on this historical data. If BI is the way to catalog the past, then BA could be called the way to deal with the present and predict the future”

#2 “BI reporting ends with the dashboard, which is sufficient only for some business planning, and BA picks up the rest for the Go-To Guys. Simply, this group must interact with data in a much different way from what traditional BI allows […] Remember, you don’t get business analytics when you buy business intelligence. The requirements are different and the benefits are different. The return on information and expertise achieved by arming your operating managers with analytics will supercharge your existing BI investment”

#3 “Classic business intelligence questions, said Davis, “support reactive decision-making that doesn’t work in this economy” because it can only provide historical information that can’t drive organizations forward. Business intelligence, he said, doesn’t make a difference to the top or bottom line, and is merely a productivity tool like e-mail.”

Data puddles, ponds, lakes.. and now swamps


I love Gartner, not so much for their research but for the great terms they create to refer to some technical challenges.  It started off with data silos (departments working independently with their own data sets) then we had lakes to refer to the vast quantity of data generated by businesses, and now we have data swamps.   I must not forget ‘dark data’ too – perhaps that is a really muddy (data) swamp.  Swamp is probably a better analogy since the data lake is typically a repository of business data with little or no governance.

“Simply a data lake is an attempt to bring together physically a number (or all?) of the data stores available in such a way as they can be easily accessible to users (data scientists).  …a data lake does not support a single version or view of the truth.  It supports “any number of views of any number of truths”.  So it might be holistic in terms of data collection; but in terms of information and insight, it is fleeting and is more of a swamp.  There is value in a data lake, but it is not the same value you can get from an agreed, shared view of the truth.”

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I am now blogging here regarding data architecture and  large scalable distributed solutions.  This is a fairly broad, technology agnostic, topic however I do have a preference for Microsoft products.

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