Quiz – July 2011: Hunh?

NOTE: Answer key appears below the quiz…

 

ANSWER KEY

We received 2,371 responses to last July’s quiz (with an error rate of +-2,365).  After extensive review and auditing, the winner was selected.  Unbelievably, the result was a tie and we’re proud of the fact that both of the winners are esteemed Dataspace alumni!  They are, Dave Johnson and Dave Schlewitt.  Congratulations, Daves!  Each will receive the latest version of the Dataspace mug.

Here are the answers.  Astute participants will recognize that, while the original quiz questions were not numbered, we’ve ascribed numbers to the answers as an added bonus:

1) The data modeling symbol represents a one-to-many relationship

2) The picture is an extremely childish representation of a star schema, that’s right folks, a star schema

3) The 1926 Stanley Cup was won by, of course, the Montreal Maroons. who also went on to win another cup in 1935.

4) The answer is, of course, all of the above.  Technically, one of the above is also correct.

5) Despite the rather loud objections of one quiz participant, the process of combining tables into larger tables is not called chubbification.  It is, instead, called denormalization.

Thank you to all who participated!

 

Quiz – January 2011: Name the BI Vendor or Candy Company

 

 

 

 

Tying the BI tool to the user

Yes, a 747 and a Cessna can both be used to transport you from point A to point B but, isn’t the 747 a bit of overkill for the pilot who just wants to fly himself to the next airport for a $100 hamburger?  Well, in Business Intelligence (BI), many organizations buy a fleet of 747s when all they need is a few Cessnas – they buy tools that are powerful but overkill for most of their users.  A great example of this is when a company buys 7,000 licenses of an expensive, powerful OLAP tool, intending to outfit their entire staff with OLAP.  Is there a need for advanced online analytical processing (OLAP) in the company?  Almost certainly.  Are there 7,000 users who are going to slice and dice through their data?  Almost certainly not.

You can think about BI needs as a pyramid, small at the top and large at the bottom.  At the very top are a few analysts who use data mining tools to identify unexpected relationships and build predictive models by looking at huge data sets (Can you remember when the data mining companies were looking to put mining on every desktop?  Mining on every desktop?  Really?).

Just below the data miners is another, slightly larger, layer of folks who need to slice and dice through their data – the OLAP users.  These folks are looking for things like what products are selling well, in what regions and by which salespeople.

Next is the bulk of the pyramid – the folks in the field who are just trying to get their jobs done.  The folks who need BI to execute specific business processes: to see which customers receivables are over 30 days old; to see where maintenance crews have been assigned for the week; to do the actual day-to-day work of the company.  Do these folks need to slice and dice through huge quantities of data?  No.  These folks generally need a set of predefined reports which have a few flexible parameters for users to complete to specify exactly what data to report on.

While the major BI tool vendors sell their tools as allowing users to create their own reports and to slice and dice their data, the bulk of the pyramid never uses this capability.  Instead, when these tools are released to users they are released with libraries of pre-configured reports.  Most users never do more than use these reports or, occasionally, request new ones.

Once you understand this reality, you start to look at the concept of BI tool standards quite differently.  More on this in a future post.

Think you’re overbuying in BI?  Drop me a line.

– Ben