What is Business Analytics ?

Here is a simple metaphor to explain the difference between Big Data and Business Analytics. If you think of Big Data as a mining operation, you can think of Business Analytics as metallurgy.

I use this metaphor because Big Data provides very raw material in very large quantities. The output of Big Data requires a great deal of work to turn it into something valuable. This is the 'value' problem in Big Data (not one of the original V's but included increasingly more). It is hard to look at the ore coming out of a mine and see expensive jewelry. And it is hard to look at the vast amount of Big Data being created and see useful data products.

Business analytics, on the other hand, takes refined data products that already exist and mixes them in attempts to find a new composite or alloy that has desirable properties not available in the component products. While it is still difficult to know before hand what value might be found in the composite data products, there are two benefits that Business Analytics has over Big Data. First, you have a better idea what the outcome might be since you are working with refined data products rather than raw materials. Second, the volume is much, much less in Business Analytics so you can afford to do more trial and error.

Here is an example of attempting to use Big Data.I should mention that this example is constructed to make Big Data comprehensible to people who are not familiar with it as most examples are a little to arcane for the average person. Let's say you get a data feed from the Internet of Things. The Internet of Things is an emerging concept which is becoming more real over time and will continue to do so. In this feed you get information from parking meters, soda vending machines, websites, smart home appliances, EZ-pass toll booths, weather transponders, automobile computers and so on. This data comes at you as it is created. It is huge in volume with a great deal of variety. You want to use if to get a better understanding of your customers but you have no idea whether they are distinguished by regular car maintenance, how often they fail to feed parking meters, or whether they have milk going sour in their refrigerators. You have a huge volume of very raw data and need to figure out how to get some value out of it.

Compare that with an example of attempting to use Business Analytics.You have a chain of grocery stores and want to know if people in different locations have different purchasing behaviors resulting from weather forecasts. So, you pool all of the sales data from all of your stores, acquire some information on weather forecasts, and look for correlations. The data you are using is much more orderly and the things you are looking for are understood much.