A Data Quality Framework

Your data’s quality and accuracy will directly affect the adoption, success, and usage of the smart360° platform. Without a common data management approach to acquire, manage, and sustain the metadata and data, the results may be random, unreliable, and can quickly diminish the value of your investment.

Jean Gehring from fi Architects Group put this guide together.

This guide outlines a 4 step Data Quality Assurance Framework to support the smart360° implementation and help you construct a data management function or program to ensure the metadata and data will be trustworthy and data decisions are grounded in fact.   The framework is condensed from multiple smart360° implementations and designed to be collaborative, transparent, accountable, and capable of eliminating bad data. It can be easily modified to meet your data quality requirements, fit your operational needs, serve as a stand-alone data management function, or integrate into your Data Quality or Enterprise Architecture Governance bodies.

4 step Data Quality framework

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