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April 21, 2010
Successful BI Requires Data Readiness
The first area of risk in BI is that of poor data quality, availability and readiness. Too often this risk is ignored, under-managed or addressed indirectly with enterprise-wide data modeling. It seems that business users usually believe that "the data is good" in their system and BI project teams want that to be true. Perhaps this is why the risks around data readiness are ignored.
But data readiness from the perspective of supporting BI that has true business value is not the same as data quality. For example, one of the first things that needs to be tested is whether or not the data actually contains the answers to the questions being asked by the BI project. A few SQL statements cannot guarantee that this is the case.
Another common example is that disagreements about data definitions or simply data with similar names meaning different things in different systems can cause the illusion of bad or wrong data.
Many other more traditional forms of data quality issues can also be problematic. Historical reach may not be long enough; the data may be incorrect, missing or incomplete; the data you need may not be collected at all; data from different source systems may not be conformable, etc.
Having a a clear understanding of what questions need to be answered (BI goals) and profiling specific data to determine "data readiness" for a specific BI objective is a great approach. Knowing the reality of actual data readiness helps to focus attention on the things that are possible and avoid wasting time on things that are not.
If data readiness is ignored, your BI project spends time designing models that can't be filled, reports that can't be built, KPIs that can't be trusted and outcomes that don't provide value.
Posted by Kelly Lautt and John E West at April 21, 2010 4:45 PM
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