Back on February 5, 2012, I published a blog post about the Mainframe Analytics Taxonomy, which divides mainframe software products along two dimensions: behaviors (the "what") and business value (the "why").
In that taxonomy, I briefly listed seven values along the "what" axis (numbered 1 to 7) and six values along the "why" axis (lettered A through F).
As I mentioned in the blog post, a given software product may have more than one value along each axis, though often its current primary focus will be in just one category.
So, I thought it might be of interest to do a quick series of brief blogs just explaining each value.
I'll begin with Data Handling, number 1 on the "what" axis:
Now, this is a very big category, and covers everything from storing and managing data in databases, to processing, combining, sorting, modifying and moving data. It's also one of the things mainframes do best.
Right from the beginning, the mainframe architecture was designed to handle massive amounts of data such as a world-class business might regularly process. One example is: all the records a national government has about its citizens' taxes. Another: all the information about a large financial institution's customers and their accounts.
One aspect of this category, databases, has deep roots that go way back. The theory of how to efficiently store and access large amounts of structured data led to the development of some of today's most important databases, including IBM's DB2 and IMS, CA Technologies' CA Datacom and CA IDMS, Software AG's ADABAS, and some distributed databases such as Oracle and Ingres which are available on Linux.
Another is data sorting, which was such an important utility that it was the first product of two important software companies that are still going strong today: Syncsort and their eponymous product, and CA Sort from Computer Associates, now CA Technologies. That was despite the fact that IBM already offered their own sorting utility for the mainframe, but the business need was such that these optimized alternatives were enough to launch their companies.
There are also many utilities designed to examine and modify and move data in many different ways. Interestingly, this is a good example of being in more than one category, since they often also have value 3: Applications and Automation. A good example of this is applications that take address data and turn it into validated mailing addresses printed on envelopes (or statements visible through windows in envelopes). But I'll get to that in another two blog posts.