What Is Business Analytics

¶ … analytics in business in 2016 is intrinsically tied into one of the most common business buzzwords of the new millennium, "big data." Whatever strategies were employed in utilizing the concept of analytics for business before the twenty-first century, it is clear that now analytics hinges almost entirely on the concept of big data. This is something that Duan and Xiong (2015) make clear in their journal article on "Big Data Analytics and Business Analytics" where the writers observe that

Any research progress in business, science, engineering, education, sociology and other areas is either driven or supported by data. Although data alone are cheap and ubiquitous, what makes data a valuable asset is the useful information hidden inside them. Since there are many different types of useful hidden information which require different analytical techniques to find, these analytical techniques become an indispensable complement to data. (1)

These analytic techniques are indispensible to business activity in the information age, quite obviously so because they hinge upon the possession of information and the ability to use it effectively.

To give an obvious example which will be familiar to many people, imagine purchasing a book on Amazon.com. Before allowing you to pay for your purchase, Amazon's website will frequently display a banner headline which says something like "People who purchased the items in your cart also purchased:" and this will be followed by a display of four or five different items that can be clicked on for additional purchase. This is a display of the power of big data and analytics working hand-in-hand. Big data is Amazon's vast record-keeping of previous transactions, so that they know (for example) that consumers who purchased "Fifty Shades of Gray" on Amazon are more likely than other consumers to purchase "The Story of O" or "Venus in Furs." To be able to spot this connection in the midst of a vast sea of data requires analytics, but those analytics can propose a business strategy for maximizing profit -- before allowing the consumer to purchase "Fifty Shades of Gray" offer the consumer a chance to buy "Story of O" or "Venus in Furs" as well. Data analytics have already told you that this particular consumer is more likely than any others to want to purchase those items -- the chance is significant that Amazon will double its sales, simply by knowing what to offer to the particular consumer at the critical moment of purchase.

These examples, however, demonstrate that analytics is essentially the art of interpreting data in a usable way. To use an example of an online retailer like Amazon again, there are any number of different ways in which the data can be analyzed. Are people more likely to purchase "Fifty Shades of Gray" if they live in Alaska, or if it's close to Valentine's Day, or if they are over the age of 65? Baker (2015) used an online diagnostic tool to show that, in fact, Alabama and Nebraska ranked highest in being interested in the film "Fifty Shades of Gray" but noted that "sometimes success in big-data fueled marketing lies more in playing to that customer's self-perception rather than appeal to their true self" (Baker 2015). It might be possible to employ analytics on previous purchasing data to find out all of these things -- certainly Amazon has access to the geography and the date of prior transactions, and probably has access to the age of the credit card holder. Otherwise it would be possible to cross-reference transactions on the same credit card to look for markers of age demographic: do people who purchase "Fifty Shades of Gray" also purchase Depends adult undergarments and Life-alert bracelets, or are they more likely to purchase the latest Justin Bieber album and a vajazzling kit? Consumer goods can correlate to age, to income level, and a host of other data points -- and it is the purpose of analytics to derive these bits of information from the existing data, in order to use them effectively to improve business performance.