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Reconciliation. The process of comparing two versions of comparable data and determining the nature of any differences observed.
Recon and control processes are necessary to complete the data quality feedback loop. It measures compliance with the business rules defined in the governance process and leads to the functional discoveries that become input to the governance process. Despite all the posturing and all the good intentions when systems are implemented, exceptions arise. Unexpected conditions occur. "Stuff" happens. And Murphy's legal staff would tell us that it usually happens in the most prominent and least opportune time and place. Closing the data quality feedback loop gives clients the opportunity to reduce the likelihood of surprises by discovering them in the recon and control process instead of less opportune times - like during your presentation to C-level executives. Obviously, this is painting the extreme case, but the better your data quality feedback loop, the better your chances are to avoid 'Murphy's Law", and the more you can control the costs that result from data exceptions. Data governance operating without the complete data quality feedback loop quickly becomes little more than an academic exercise. den Karte assists clients with implementing reconciliation, validation, and control processes.
den Karte adapts the approach to each client's unique control standards, data flows, and data quality needs. |
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