Business intelligence (BI) and analytics are all the rage in higher ed these days, but I sometimes wonder what people really mean when they use these terms. Depending on the individual, their scope of work and their sphere of influence, each of these terms can mean many things. (Boy, I love our language!) So in this post, I’m going to discuss the difference between BI and analytics, identify the different ways the term analytics can be used in our industry and provide a framework that can be used to discuss this topic at your institution. To start, let’s look at some definitions.

Wikipedia defines business intelligence as “a set of theories, methodologies, processes, architecture and technologies that transform raw data into meaningful and useful information.” In other words, BI simply refers to anything that supports the organization and delivery of information. As BI professionals, we tend to discuss this process in terms of an information hierarchy that builds from data (raw statistics) at the bottom up to wisdom (actionable, contextualized knowledge) at the top. For reference, here’s a common visualization of this concept:


Essentially, BI includes analytics, but only as one part of the whole. It also encompasses operational reporting, data architecture and any other activity whose goal is to organize, make sense of or communicate data in an effort to move from data up towards wisdom. In higher ed, this would include all of the day-to-day reporting your institution does to keep the lights on, from your financial statements to your class rosters. It also includes any dashboards you may have available, at any level of the organization. What’s most important is that we understand that the term is very broad and, when used appropriately, could mean anything within the scope of your institution’s reporting ecosystem—like analytics, for example.

“Analytics,” says Wikipedia, “is a two-sided coin. On one side, it uses descriptive and predictive models to gain valuable knowledge from data – data analysis. On the other, analytics uses this insight to recommend action or to guide decision making – communication.” This is a useful definition, since it covers both the communication and the making sense of data. That said, Wikipedia also has a shorter, simpler definition, which defines analytics as “the discovery and communication of meaningful patterns in data.” To me, these meaningful patterns are really the lynchpin of good analytics.

The discovery of meaningful patterns is what allows analytics to move us in the hierarchy from information up to knowledge and wisdom. In practice, this involves an array of tasks ranging from descriptive statistics (which are typically displayed graphically) to predictive models, what-if scenarios and correlation analyses. In my opinion, activities that result in one-off, list-style reports would not be considered an analytical activity, since they do little to highlight patterns or trends. On the other hand, while ad-hoc elements like interactive dashboards or OLAP cubes are often useful for analytics, they are not necessarily required for an activity to be considered analytical.

Now that we have a firm grasp on the relationship between BI and analytics, let’s look at the table below, which provides an outline of the big picture questions that your analytics should be answering:

Analytics and BI table

As you can see, there are various categories you can use to help frame discussion as your institution’s decision-makers work through the problems they’re attempting to solve. The descriptive questions, which look at how things are and have been, should be your first stop. Building on those, the prescriptive/predictive analysis will give you a sense of what action you should take and what might happen in the future as a result.

Let’s say you’re trying to answer a common question, like “How many students were enrolled last year vs. this year?” You’ll start with descriptive analysis, which will include data related to both historical and current information. You’ll also start to gain some insight as you discover how and/or why the numbers are the way they are. From there, you’ll want to start to answer the prescriptive questions. Causal relationships and correlated statistics can be used to predict future scenarios, and their likelihoods, which will enable the administration to be more proactive in their decision-making.

I hope the definitions discussed here will help you navigate some of the common BI pitfalls caused by poor communication and ambiguous terminology. Help your team grow in understanding and further your institution’s mission by using data the way it was meant to be used: to inform and eventually lead us to knowledge and wisdom.

What guidelines do you rely on to guide the BI or analytics at your institution? Share them with us, in the comments section below!

Share This