This is the first in a series of articles talking about general report capabilities of an enterprise software system.  In this series, I’ll be referencing our Visions Server Foundation along with our FAMCare case management and Patient Registry products.  But what I write about can apply to almost any larger scale system.

Canned Reporting vs. Tailored Analytics

Typically, systems will have “canned” reports, and these are available on demand, usually with flexible output and sorting options.  The software manufacturer in this case has anticipated what most users may need. 

There are usually many reports that are supplied, and only a subset of these would be used by a typical customer.  The idea is that a customer’s needs are more than covered in this set of reports.

In addition to canned reports, many advanced systems will have some type of “ad-hoc” querying or reporting capabilities.  This allows somewhat proficient users to tailor or even create their own reports based on most data elements within the system. 

For larger enterprise systems, often the manufacturer will supply customized reports that supplement the “canned” reports, and these are added at customer request.

Most of the time, reports are used to create a snapshot of current financial or other activity in the system, or perhaps a recent time period.  These reports are used to project the current financial health of the company (or recent period), or possibly, as in our FAMCare product, to make sure compliance is met to meet standardized governmental reporting requirements. 

But when reports are used only in this manner, you may not be getting all of the benefits of the data you collect. I’ll use one very simple example to show how “normal” reporting requirements may not give you the entire picture. 

A national company selling a generic product noticed that from their standard reports, they had peak sales both in the spring and fall periods of the year. Without further data, it was assumed that these peaks were normal seasonal variations.  But when one their analysts delved further, they found that by matching gender to sales, they determined that in the spring, 80% of their customers were female, and in the fall, 75% of their customers were male.  By putting in more demographics, they also found that in the spring, most of their females were between 30 and 40 years old, but in the fall, most of their male customers were between 40 and 50. 

This is very important data that would not be found in standardized reports!  Knowing this information, the company was then able to target its advertising at the appropriate time of year.   

Stay Tuned...

In the next post, we’ll look further into tools available to provide this type of analysis.