An Explanation In Plain English Of What Business Intelligence Actually Is, With Two Real World Examples

OK, Explain It To Me

Companies and organizations now feel that they need “Business Intelligence” (BI) to make sense of the data that they gather in order to make better decisions. However, they can be awfully vague on what that means. Before I got hired to work in Business Intelligence, I used to wonder about what on earth they were talking. So what are employers actually doing when they say that they are creating “Business Intelligence”?

The answer is straight forward once you get past the buzzwords:

Business Intelligence means to use SQL queries and scripting to extract information that a human being can easily digest from an organization’s database or databases.

That’s It?

Yes, that’s it. It’s a branch of data analysis.

Fantastic, I am going to set up Business Intelligence for my organization tomorrow!

Now hold on there. Just because one can explain BI easily in one sentence does not mean that it is easy to do. There are at least four major barriers.

  1. The SQL queries can be very complex. They can take days or even weeks to write.
  2. Scripting, using a stats package like SAS or R, or a reporting program like Crystal Reports is often needed in addition to SQL to get the answers you need and to present it in an easily digestible format.
  3. The previous two statements assume that the databases, querying applications, and other software have been set up well in the first place, which often takes a large staff beyond those in an organization’s BI section.
  4. The data needed to do the analysis may not be currently collected. Developers’ of live business databases main priority is usually to ensure that an organization’s information infrastructure runs smoothly from day to day. It is very likely that it never occurred to your organization’s developers to record the data points that you need.

OK, fine, it’s tougher that it looks, but I want to see these real world examples so I can have some idea what I should expect.

No problem.

I can actually give you two good examples of Business Intelligence which I helped create. I used to work for the marketing company LivingSocial as a Business Intelligence Analyst. While most of my work was proprietary, I did BI for two articles for LivingSocial’s Blog.

  1. What are the Nicest Cities in the US? – discusses which of LivingSocial’s American cities have the largest percentage of gift purchases
  2. Who’s Leaving on a Jet Plane? – discusses which of LivingSocial’s American cities have the largest and smallest percentages of travel purchases

I wrote the SQL queries that returned the percentages and tables used in both blog posts. While both are simple examples of BI, they are very typical of the information that organizations wish to get from their data.


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Why Did The Arizona Cardinals Miss The 2013-2014 NFL Playoffs?

Because there are now 32 teams in the NFL, up from 28 in 1990 when the 16 game season started, while there are still only 12 playoff spots. 10 – 6 is not good enough anymore to almost guarantee a playoff spot.

Read the full analysis here.


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Hilarious Meme About Being A Data Analyst

I found this What People Think I Do / What I Really Do meme about Data Analysts while doing a Google Image Search for Data Analysis. I promise that you’ll get a kick out of seeing it. The last three pictures are perfection.

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Does A Record Of 10 – 6 Guarantee A Playoff Spot In The NFL?

One thing that I have noticed in recent years while following the National Football League is that teams with 10 wins and 6 losses miss the playoffs more than in the past. Am I just seeing things, or has have things slowly changed since 16 game seasons started in 1990? If I am not seeing things, what caused it? Parity? More teams? I decided to take a look. Here is what I found:

Year Total NFL Teams Teams 10-6 In Playoffs Not In Playoffs
1990 28 2 2 0
1991 28 3 2 1
1992 28 2 2 0
1993 28 3 3 0
1994 28 3 3 0
1995 30 3 3 0
1996 30 4 4 0
1997 30 3 * 3 0
1998 30 3 3 0
1999 30 2 2 0
2000 31 4 4 0
2001 31 3 3 0
2002 32 4 * 4 0
2003 32 6 5 1
2004 32 3 3 0
2005 32 3 2 1
2006 32 3 3 0
2007 32 5 4 1
2008 32 1 1 0
2009 32 3 3 0
2010 32 5 4 1
2011 32 3 3 0
2012 32 5 4 1
2013 32 2 1 1

* In 1997, The New York Giants were 10 – 5 – 1. In 2002, Pittsburgh was 10 – 5 – 1. I considered that close enough for 10-6 for these purposes.

As one can see, before 2003, only the 1991 season had a team with a 10 – 6 record that missed the playoffs. Then in 2003, 2005, 2007, 2010, and 2012, one 10 – 6 team missed the playoffs. This does appear to be a recent trend.

So what caused it? Parity or More Teams?

As far as parity is concerned, I would argue that parity started in the 1997 season, three years after the salary cap took effect. The AFC finally won a Superbowl that season after losing for 13 years straight. Superbowls that came after tended to flip more between the two conferences each year while far fewer were blowouts with one team crushing the other.

I suspect the reason for the recent increase in seasons with at least one 10 – 6 team missing the playoffs is the increase in the number of teams. In 1990, the NFL had 28 teams. By 2002 it had increased to 32. However, the number of playoff spots stayed fixed at 12.

Think of the playoffs as a pie that has exactly 12 slices. Some of those slices are bigger and tastier than others. A bye week with home field advantage throughout the playoffs is the biggest and tastiest slice of pie. The smallest and blandest is a wild card with no home field advantage. However, even as the league added more teams, the slices of playoff pie have stayed fixed at 12. 28 teams shared those 12 slices in 1990. Since 2002, 32 teams have fought for the same 12 slices of pie. Therefore, one now has to struggle more to get a slice of playoff pie, causing a 10 – 6 record to be less likely than it used too to get you a playoff spot.

Up Next

How to make a nice HTML table like the one above in Excel..

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