Business Intelligence
Business Intelligence vs Business Analytics As Seen Through Football
Let’s say you’re on the coaching staff of a football team and you want to review the most recent game. You do this to see how you can fix your errors and replicate your successes.
Using our previous definitions, BI would be the process of identifying all the statistics and plays that led to your team winning. It would identify that you kept possession of the ball for much longer than your opponents. It would also identify the trend that your right side of the field was instrumental in retaining possession through excellent passing.
Business analytics would be more concerned with why you had possession of the ball for longer than your opponent and why your right side of the field did so well at passing.
Was it because:
Your opponent’s defenders on that side were weaker players than their defenders on the other?
Your right-side players had been putting in more time on the field together then your left side?
One of your players on the right was simply having a phenomenal performance which carried over to the rest of that side?
These questions are important. They allow you to figure out how you can replicate your success, or prevent your failure in the future. Asking the right business intelligence questions will lead you to better analytics. While using a business dashboard, all the insights can be simplified into a single place, making the time for meaningful decisions much faster. But first, we need to analyze the difference more, as that will help us to understand what to do in a company’s operation process, and how to chose the best tool to manage your insights.
Without further ado, let’s dive deeper into the difference between business intelligence and data analytics. In order to do so, we need to examine the distinction between correlation and causation.
Correlation Is Not Causation
When two things are correlated, it means that when one happens, the other tends to happen at the same time. When two things have a causal relationship, it means that one thing leads directly or indirectly to the other happening.
A famous example of the difference between these two is the fact that ice cream consumption and city homicide rates are highly correlated. Now, of course, ice cream does not cause people to murder each other. So clearly there is not a causal relationship.
The two are correlated due to the fact that homicide rates rise when temperatures rise in the late summer. It is theorized that since warmer weather brings more people outside, this leads to more social interaction, some of which is violent.
You Can’t Always Trust What You See
You can find examples of people confusing correlation and causation everywhere you look. For example, that muscular person at the gym who always likes to give you work out advice may or may not actually know what they are talking about. The advice they’re giving you, while correlated with being known by a muscular person, may not actually lead to being muscular. Instead, they may simply have good genetics. They may be muscular not because of their knowledge, but actually in spite of it.
Moving into the lighter side of things, there are some hilarious examples of things being correlated that clearly don’t have a causal relationship. Many of them are shown on the website Spurious Correlations. For example, divorce rates in Maine are very closely correlated with per capita consumption of margarine… Maybe married couples should switch to butter instead?
In all seriousness, it can be extremely difficult, depending on the field, to separate correlation and causation. Very large scale and expensive research trials are often done just to find evidence of causal relationships. Also, a famous example would be the butterfly effect. But we won’t go that much into details, and, actually, examine more the business side of things, and, therefore, concentrate on the specifics of business intelligence vs data analytics, and provide insights on correlation and causation in the business realm.