What is Business Intelligence?

What is Business Intelligence?

Business Intelligence (more commonly known as BI) is a term that increasingly gets thrown around a lot. If you are a sports fan you may have heard terms like Corsi or Fenwick during a hockey broadcast, or WAR and WPA during baseball. In and of themselves these terms aren't strictly BI, but they are a good representation of how good BI can change how you see and evaluate the world around you.

Business Intelligence is a combination of tools and statistical analysis that can give any business an edge. Combined with Big Data, BI can and should play a role in corporate decision making.

Some of the most common areas where BI can make a big difference are customer profiling, market segmentation, and inventory analysis, but BI thinking can be applied to any data that you have managed to acquire and store.

Key BI Features



Much like the dashboard of a car delivers key information for operating a motor vehicle, a BI dashboard should deliver easy to understand but personalized data to your users. This usually means focusing on keeping things at a summary level and focusing on key performance indicators (KPI) wherever possible.

There is nothing wrong with allowing a user to dive deeper should they see something that catches their eye, but displaying too much information at the dashboard level leads to sensory overload, and user apathy towards BI.


The key difference between BI and a traditional report is interactivity. A good BI implementation allows for the user to interact with the data, so that they can better understand what it is they are looking at and get more out of it.

A good BI report will encourage and allows users to drill down to see the detailed data behind the summaries. Users can then filter the data so that they can eliminate white noise and focus on a specific aspect of the report.

Visual Cues

A good BI implementation will include the proper visual cues to help user's digest the information as quickly and easily as possible.

This includes simple things like representing numbers with pie charts or graphs instead of numbers, or conditionally formatting data to highlight data exceptions, but also includes the idea that your reports should have a natural flow to them, so that users can follow your report from beginning to end without stumbling in between.


Each of these two reports shows the same data, but the report on the left is much more effective at conveying its results than the report on the right.


Location Intelligence

Location Intelligence is pretty much what you think it is, it combines data with geographic (mapping) graphics.

By gathering and investigating location based data, you can identify and create business strategies that revolve around where your customers are located, in combination with how they use your products. It makes for a powerful combination.

What If Analysis

Once you have a solid understanding of your data and have represented it through one or more BI offerings, you can leverage your hard work by presenting the tool several "What If" scenarios to see how the underlying numbers change.

Performing this type of analysis lets you avoid the pitfalls of a "hit or miss" strategy and leads to better planning.


If you do decide to pursue BI within your own organization, don't fall in to the trap of building reports to show you what want to see, or start basing your decisions based solely on data.

BI isn't a replacement for the traditional decision making processes, but should be used to augment what you already do - effectively aligning your head and heart.

Concentrate on asking the questions you most need answers to and let the data show you where you stand.

Sherpa is committed to the cycle of analysis, insight and recommendation. Whenever possible, we build measurability into our marketing efforts. This commitment to analytics has helped Sherpa differentiate from its competitors. Sherpa's understanding of analytics is helping its customers gain that same advantage, helping them get ahead of the game.

Glossary of Terms

Big Data: the collection of large and complex data. So large and complex that traditional evaluation tools (like Excel) fail to produce meaningful results.

Corsi: The number of shot attempts by a team or player.

Fenwick: The same as Corsi, except it excludes shots that are blocked.

Both Corsi and Fenwick are used to evaluate puck possession, which is essentially how much your team or players have the puck versus the opposition. A lot of statistical pundits predicted the Maple Leafs collapse during the 2013-14 season because their Corsi/Fenwick numbers were so low. 

WAR: Wins above replacement. The idea here is that a team can always cheaply and easily replace a major leaguer with an average player from AAA. WAR is an attempt to measure how much better or worse than the average AAA call up a given player should be.

An interesting fact about WAR is that there are only two websites that calculate WAR, yet each does so differently using their own proprietary formulas (Baseball-Reference.com and FanGraphs.com).

If you follow baseball, you likely would have heard the WAR stat thrown around a lot when discussing who should have won the 2012 and 2013 AL MVP awards between Miguel Cabrera and Mike Trout.

Cabrera won in 2012 thanks to winning baseball's Triple Crown - 44 HR/139 RBI/.330 BA. Trout hit 30 HR/83 RBI/.326 BA that year, which put him in 13th/23rd/2nd in those categories in the American league, but beat Cabrera handily in FanGraphs WAR: 10.1 vs 6.8. In fact, Cabrera didn't even finish 2nd in WAR in the American League that year - Robinson Cano of the Yankees was 2nd with a WAR of 7.7.

In 2013 the divide between the two was narrowed, but Trout still beat Cabrera handily, 10.5 to 7.6 (finishing 2nd that year ahead of Cabrera was Josh Donaldson of the Athletics, whose defense pushed him barely ahead of Cabrera).

Does WAR alone dictate that Trout should have been the MVP winner over Cabrera? Not in my opinion, as no one number should ever be used to make any decision; I do agree that it certainly should have been a larger part of the discussion though and not ignored because of traditional baseball numbers.

WPA: Win Probability Added. This is an attempt to measure how much a given player increases or decreases his teams probability of winning a given game. Hitting a home run or making a run saving catch would positively affect WPA, whereas committing a fielding error or getting caught stealing would negatively affect WPA.


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