Differentiating Business Intelligence and Analytics

Differentiating Business Intelligence and Analytics

I have been in forums and discussions where people interchange the terms “Analytics” and “Business Intelligence (BI)” as if they were the same. Sometimes I feel it is even acceptable to do so. Also, I have noted they prefer to use the term “Analytics” (when referring to BI) since it is a buzzword and sounds chic, perhaps.

In fairness, drawing the line between Business Intelligence and Business Analytics is not easy sometimes.

In my mind, the line can be drawn according to how we manipulate the data and the type analysis we are performing. Let me explain this in a better way: BI will allow us to do a full data management cycle and understand our current state of it. It will allow businesses to be operationally efficient.

Instead, Business Analytics is about analyzing opportunities or trends that are projected in the future (e.g. forecasts or predictions). A very important keyword that makes a difference is “models”. BI does not involve any modelling, while Business Analytics does.

I need to highlight those capability sets are not mutually exclusive; in fact, there is a lot of overlap across them. Hence, the reason why I mentioned it was sometimes difficult to draw the line between both domains.

A business must have Business Intelligence capabilities to survive; however, an advanced Analytics capability would provide the business with greater competitive advantages.

On the other hand, both set of capabilities are required in a business to run make a difference, innovate and gain some edge. It is obvious to me that in order to perform a predictive analysis, we would necessarily need to understand the current state of the attributes we want to predict. At the end of the day, those predictions or forecasts will allow our business to get some insights.

I saw a chart in a presentation some months ago, which inspired me to come out with my own. Basically, if we needed to draw a line across those three disciplines or domains, they would look like this:

With the vast amounts of data we deal with today, it is not uncommon to have data warehouses that could be stored in disk arrays or even a cloud. Dealing with those would fall under the Big Data domain – in case the matter was not complex enough. Big Data would address the typical three concepts: volume, velocity and variety.

Big Data should not be dealt in a different domain than BI or Analytics. In fact, I see a very close relationship, where Big Data is a more generic concept than the other two.

Another aspect of differentiation is that Business Analytics typically would assume the data has been organized and cleansed for it to perform any actions. More often than not, BI would deal with the ETLs, data quality, etc., so that the Business Analytics stage can take place.

(Disclaimer: This document reflects the author’s personal views and not the views of organizations where the author is affiliated.)

Article written by Jaime Noda
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