A Working Definition for Analytics

It's Christmas season and your wife finally got you to agree to mail the Christmas gifts. Perturbed, you drag the small hill of packages to the local post office, only to see a half mile long waiting line and two very indifferent postal clerks taking their sweet time handling the customers. Two hours later you are still standing in line, very cross with the U.S. Postal Service and your wife for wasting your entire afternoon. Next year, you decide to make the trip to UPS instead.

The U.S. Postal Service has just lost a customer. Coincidentally, USPS does gather data on how much time customers spend at the window. Unfortunately, it also gathers data on 2000+ other matrices. Critical information such as customer wait time often gets overlooked because managers are bombarded with too much information. Adding another postal clerk could have brought down the wait time from 2 hours to 45 minutes. Adding two more postal clerks would bring that wait time to 15 minutes. Adding three more postal clerks would bring that wait time to 5 minutes. Customer satisfaction surveys show that postal customers start getting agitated if wait time exceeds 18 minutes. Consequently, we can see that adding one more postal clerk would not have improved the situation, adding three more postal clerks would have resulted in the postal service incurring unnecessary costs. Therefore, there is an optimal number of postal clerks that the USPS could have assigned to the above mentioned post office, and that number is four. The postal service could have retained their customer if they had a good analytics strategy in place. So what is "analytics"?

The most simple definition of analytics would be that it is "the science of analysis". In reality, the word "Analytics" has not been properly defined by the professional community and may mean different things to different people. A simple and practical definition, however, would be how an entity arrives at the most optimal and realistic decision from a variety of available options, based on existing data. There are several aspects to this definition. First, the purpose or goal of this endeavor is to arrive at a decision. Second, the process should be able to identify the best option from a range of available options. Finally, the decision making process should be based on data. Business managers may choose to make decisions based on past experiences or rule of thumb, or there might be other qualitative aspects to decision making; but unless there is data involved in the process, it would be considered beyond the purview of analytics.

Many people think that analytics only involves the use statistical analyses or mathematics to predict and improve business performance. It would, however, be erroneous to limit the field of analytics to only statistics and mathematics. Good analytics professionals should be well trained in business concepts and the social sciences, as well as have a good grasp of statistics and mathematics. A good analytics professional should be willing and able to work across various fields to come up with the proper solutions. Others argue that an analytics professional should also be cognizant of his data sources, which includes knowledge of his organization's IT infrastructure (how else would he know that his data is not being compromised or corrupted by the system). That is why analytics is unique and much broader than the use of statistics or mathematics in business.

With computers getting more powerful along with the increasing popularity of Business Intelligence (BI) tools, the importance of analytics is growing for businesses. Analytics has been credited for helping Netflix ward off competition from Blockbuster video and helping Google overtake Yahoo! to become the most profitable portal on the internet.