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Data Governance Strategy

Data Governance is often viewed as set of unnecessary rules and standards, which stand in the way of business progress. We would argue an unintrusive Data Governance can be accomplished through partnership with business stakeholders.  Once the business stakeholders understand the benefits of having a cohesive data environment, they will also learn that Data Governance can be an instrument of efficiency. Through a shared responsibility for the data quality, more trust in data will be established, and there will be an increased usage and sharing of data.



Following data management disciplines are some of the important components within Data Governance Strategy:

  • Master Data Management (MDM), or Reference Data Management - this is perhaps the most important discipline within Data Management, where data from across different Data Silos are brought together for:

    • Shared definition of reference data (i.e., Customer, Product, Security, etc.)

    • Shared Classification and Taxonomy of Reference Data (please note - there can be multiple classifications based on different usage of the data).

  • Standardization - Standards in Units and Measurements across the organization

  • Metadata Management - Metadata is what describes or gives context to our data

    • Data Lineage - ability to show the processes and changes that our data objects go through before it gets to a database. 

    • Data Profiling - An automated process to collect statistics on data elements for the purpose of data quality and consistency .  

    • Business Glossary - Catalog of the Business Terms and Data Objects and their relationships

  • Audit and Reporting - Identification and monitoring data risk areas

  • Information Lifecycle & Change Control Management




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