From the Professor: Business Data Analytics

By: Dr. Bob Boncella

“Let’s look at the numbers before we decide…” or “The analytics say we should…” How often have we heard these or similar phrases before or during a decision-making process? The decisions being made range from simple operational decisions like daily inventory restocking of a supermarket’s shelves; or tactical decisions of when is the best time of day to do the restocking; or strategic decisions of when to expand the store’s retail space.

Decisions Need to be Made

Managers are paid to make decisions, so they should make good ones. A good decision is one whose outcome meets the expectations of the future. If we could only predict the future, decisions would be much easier. Whether the decision is operational, tactical or strategic, there will always be the risk of making an unwise decision. However, that risk can be minimized by analyzing data that is relevant to the decision being made. Data analytics helps to predict the future or at least reduce the risk of making a poor decision.

Data Analytics

Data alone is not sufficient to allow for a good decision; analysis of the data is needed. As Wikipedia states: analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails data patterns towards effective decisionmaking. These patterns are useful in creating a model or a rule that can be used in the decision-making process. An example of this is a supermarket scenario where the data collected by checkout scanners is analyzed and useful correlations may be found. For example, data shows that most of the time when a customer buys a ready-to-cook pizza, they also buy soda drinks. So, the tactical decision to be made by the manager is to stock soda next to the pizza display.

Growth of Data Analytics

Data analytics is used in many business and non-business fields. The reasons for the rapid growth of data analytics is a result of four factors.

1. The ease of collecting data (e.g., user OCR scanners in retail business).

2. The ability to store and easily access massive amounts of dates (e.g., the use of “the cloud”).

3. The ability to process the data faster (growth of CPU capability).

4. Most importantly, the utility of results of data analysis in making better decisions.

Conclusion

The foregoing was a “25,000 foot” view of data analytics, mostly from a business perspective. Hopefully, you will come away with a better understanding of this decision support process. And appreciate its range from simple techniques, like creating charts and drawing trend lines when performing descriptive analytics; creating linear regression models when performing predictive analytics; and develop complex data analysis needed when performing prescriptive analytics.

And finally, remember, if you are going to make a decision, then it is best to “look at the numbers” and do the analytics. One cannot underestimate the importance of data and its analysis when making decisions.