Data Quality

Problem:

Our team needed to create a data set listing retail stores belonging to different territories for performance tests that involved store selection.  The data set was to be used by virtual users signing-in as different territory owners.  The challenge was that the territory and store numbers in the data set should be as random as possible.  For example, if there are 20 territories and each territory has 10 stores, the data set should contain the 20 territories with their first stores, followed by the same 20 territories with their second stores and so on.  

How often do we find the currency and gold lying unprotected in the office cubes or corridors?  How often do we find piles of garbage in and around the office buildings? Even if we do find them occasionally, how often do we find the gist of it getting summarized, packaged and sent to most of the senior managers, along with many other goodies undetected?

Information Management recently sent around their pick of best IM blog articles of 2009.  Among them was Forrester’s James Kobelius’s reaction to Bill Inmon’s “incineration of a straw man concept that he refers to as ‘virtual data warehousing (DW).’”

Testing in large scale Business Intelligence (BI) projects face challenges in data quality assurance, metrics / aggregation rules verification, source to target mapping accuracy, the test cases to the requirements traceability, and anomalies in the dimensions to facts relationships. 

Oracle Business Intelligence Enterprise Edition (OBIEE) is a BI tool that addresses quite a few of these challenges by the nature of the product's growth strategy.  As Gartner puts it, "70 functional and industry-specific packaged BI applications built on the Oracle BI Enterprise Edition Platform attests to Oracle's understanding of how to leverage the market interest in domain-specific and prepackaged solutions as a growth driver for its platform."  Customers who buy OBIEE typically also buy a relevant packaged solution.

It is a commonplace to say we should manage data like a resource. But when you think about it, data is an asset but not a resource.  Data isn’t a thing like real estate, employees, or customers, but rather it represents all of those things.  In data-geek-speak, data is a meta-resource that holds information about resources.  That makes data a lot like money.