If someone puts you on the spot, could you tell him/her what the difference between business intelligence and analytics is? If you feel a bit uncertain about the specifics here, you’re not alone. Business intelligence experts aren’t in agreement either!
We already saw earlier this year the benefits of Business Intelligence and Business Analytics. Let’s dig deeper now and figure out what this is all about, what makes them different, and how they are complementary to each other.
What Do The Experts Say?
In an article tackling Business Intelligence and Business Analytics, Better Buys asked seven different BI pros what their thoughts were on the difference between business intelligence and analytics. Each and every professional had a different take. Here are a few snippets of their opinions:
“Business Intelligence is needed to run the business while Business Analytics are needed to change the business.” – Pat Roche, Vice President of Engineering at Magnitude Software
“Business Intelligence is looking in the rearview mirror and using historical data. Business Analytics is looking in front of you to see what is going to happen.” – Mark van Rijmenam, CEO / Founder at BigData-Startups
“What’s the difference between Business Analytics and Business Intelligence? The correct answer is: everybody has an opinion, but nobody knows, and you shouldn’t care.” – Timo Elliot, Innovation Evangelist at SAP
Well, what if you do care about the difference between business intelligence and data analytics?
Your Meaningful And Simple Definitions
It seems clear that there isn’t one standard “correct” definition for the differences between the two terms. The varying opinions given by the experts is evidence of that. So, instead of trying to find the “right” answer, let’s find a useful distinction between the two that can be used simply and clearly to help you in your work. The most straightforward and useful difference between business intelligence and data analytics boil down to two factors:
- What direction in time are we facing; the past or the future?
- Are we concerned with what happened, how it happened, or why it happened?
Keeping in mind that this is all a matter of opinion, here are our simplified definitions of business intelligence vs business analytics.
Business intelligence – Deals with what happened in the past and how it happened leading up to the present moment. It identifies big trends and patterns without digging too much into the why’s or predicting the future.
Business analytics – Deals with the why’s of what happened in the past. It breaks down contributing factors and causality. It also uses these why’s to make predictions of what will happen in the future.
Confused yet? Let’s use an example from football as a metaphor to help clarify things.
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, business intelligence would be the process of identifying all the statistics and plays that led to your team winning. Business intelligence 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.
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 workout 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?
Source: Spurious Correlations
** Click to enlarge **
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.
How Does This Apply To Business?
Can you understand the factors that are causing your business success or failure rather than just the factors that are associated with your business success or failure? If so, it’s much more likely that you will be able to predict the future in your marketplace and act accordingly. However, it’s important to note that you need to know what’s correlated with something before you can know causation.
In other words, you need to know what happened and how it happened (BI) before you have the ability to say why things happened (BA) with any reasonable degree of certainty.
That is the difference between business intelligence and analytics, and that’s why both of them are crucial. They fit together like two pieces of a jigsaw puzzle – a puzzle that helps your business to be more profitable.
A Use-Case Scenario
Enough with the descriptions and metaphors. Let’s solidify things and wrap up this post with a business example.
Let’s say you work for a marketing firm that uses both business intelligence and analytics to help large e-commerce companies launch new products. In order to understand what new products would be most likely to succeed (analytics), you would need to figure out:
- What products had been most successful in the past (business intelligence)
- The seasonal trends that had influenced success for past launches (business intelligence)
- Why customers bought the past successful products (analytics)
For example, let’s say that your hypothetical e-commerce store sold boutique women’s fashion. You will need to work with your retail analytics to understand what products will work.
First, you would examine what categories of clothing are driving the most profits. Then, you can examine what times in the year those successful products had been launched. Finally, you could do a series of in-depth customer interviews in order to figure out why customers liked those pieces or categories more than the others.
If you did enough market research, and you had a large enough sample size, you should be able to predict with a great deal of accuracy what new products would be likely to succeed.
This could lead to surprises in the way that you think about your products because your customers often have a very different way of looking at your products than you do.
BI and Analytics Dismantle Assumptions
For example, maybe your assumption was that your customers mainly cared about the price point of your garments.
After your research, however, you found your customers were actually willing to spend more on your products if you emphasized your humane sourcing practices, such as not utilizing sweatshops.
Then, your focus would be on continuing to use that positioning in your marketing messages as opposed to worrying about the price points of your garments so much when doing a product launch.
The above example illustrates one of the fundamental important points of business intelligence and analytics. Your assumptions about your company, your customers, your marketplace, and your products, are often flat out wrong – or at the very least, incomplete. After asking the right questions, analytics are here to help – whichever your industry or sector, be it healthcare analytics or financial business intelligence, you need to use both of BI and BA for success.
Through the two complementary uses of business intelligence and business analytics, you can unpack your assumptions and get more accurate and useful data. Put simply, BI and BA give you the tools to see reality as clearly as possible.
This is a competitive advantage that you cannot afford to ignore. Start applying your newly acquired knowledge about “BI vs analytics” with a 14-day free trial of datapine’s bussiness intelligence software!