Can we enforce data integrity in a company to ensure accurate data?

What data governance steps can companies take to ensure that they're using "unbiased data" to make important business decisions? In other words, how can we enforce data integrity throughout our organization and make sure that data is being presented accurately in systems and internal reports?

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This is a great question, one that we've been addressing with a number of clients. While we are big advocates of validating data as it enters a system, that only allows you to verify conformance against a limited set of already-known potential errors. If your expectations for enforcing data quality as it is presented to users are aligned with that same set of known data problems, instituting validation procedures at any data handoff will at least provide some level of auditability.

However, the deeper question (or "the question behind the question") could almost be whether one can ensure consistency in the interpretation of presented information. In other words, when two individuals look at the same report, can we presume that they will draw the same conclusions? The challenge here lies less with the accuracy of a specific data value itself and more with the issue of alignment on along the definitions associated with the data element in question. For example, when a report details the number of customers, do all readers use the same definition of customer to process that result? Ensuring that there is internal alignment on data interpretation becomes a byproduct of effective data governance and the institution of good metadata management practices.

This was first published in April 2010

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