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.
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.
- The SQL queries can be very complex. They can take days or even weeks to write.
- 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.
- 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.
- 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.
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.
- What are the Nicest Cities in the US? – discusses which of LivingSocial’s American cities have the largest percentage of gift purchases
- 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.
Related books picked – and if possible read – by me. Sponsored by Amazon Associates.