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Data Quality and Data Governance

Intro

Data quality and data governance are two essential components of enterprise data management strategies that need attention. Data quality is about the accuracy, completeness, consistency, and timely availability of an organization’s data. On the other hand, data governance is all about implementing processes and policies that observe data quality across an organization. These terms may sound familiar to you, but do you know what role they play within your company?


Data Governance

Data governance is the process of establishing, aligning, and securing data within an organization. It aims to ensure that data is collected, stored, processed, and disposed of consistently. Data governance covers the strategies and processes needed to manage enterprise data effectively to leverage it for business decision-making. It also provides a framework for managing the risk associated with businesses in an uncertain regulatory environment. In short, data governance is about managing all organizational information assets — not just data but also documents, applications, networks, configurations, and metadata


Data Quality

Data quality is essential for businesses today, no matter the industry. Quality data enables better decision-making, enhanced customer experiences, and compliance with laws and regulations. It powers upper management and other stakeholders to make informed decisions. Poorly managed or inaccurate data can lead to costly mistakes for the organization. You wouldn’t want to risk delivering a false annual report! On top of that, having accurate customer information allows companies to provide personalized services and meet their needs more effectively. Benefits such as brand loyalty, repeat purchases, free publicity, etc. Moreover, having up-to-date and pristine data sets is key to staying compliant with laws and regulations while minimizing any disruption caused by errors or omissions


Data Governance and Data Quality Combined

The relationship between data quality and governance is symbiotic; they are necessary to achieve sound enterprise data management. Data governance provides the framework for data quality, and data quality provides the foundation for data governance. Data governance is responsible for ensuring that data is managed correctly, while data quality is responsible for ensuring that data is accurate, complete, and consistent. Together, they can help your business improve its bottom line by providing better visibility into enterprise assets, all while driving efficiency and operational improvements that lead to greater business agility.


Data governance and data quality efforts have many areas of common interest. Both programs share a mutual interest in issues of policy, data cleaning, and data consistency. Data governance is a set of practices that supports formal data asset management. Data quality includes activities that support accurate, accessible assets across the enterprise. Both require ongoing efforts; they are a marothon, not a sprint.

Conclusion

Data quality and data governance are two essential components of enterprise data management strategies that need attention. Data quality is about the accuracy, completeness, consistency, and timely availability of an organization’s data. On the other hand, data governance is all about implementing processes and policies that observe data quality across an organization. The relationship between data quality and governance is symbiotic; they are necessary to achieve sound enterprise data management. Together, they can help your business improve its bottom line by providing better visibility into enterprise assets, all while driving efficiency and operational improvements that lead to greater business agility. By improving data quality, lowering data management costs, increasing access to needed data across the organization, lowering risks of errors being introduced, and ensuring that clear rules regarding access to data are both set, enforced, and adhered to, data governance can help organizations to reduce costs, improve efficiency, and increase customer satisfaction.


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