This is exactly where the data governance system comes into play. Who can do what, when and with what data. All of these questions should be answered through data governance so that data remains only accessible to authorized people. This is important because regulations such as the EU GDPR set strict limits on the use of sensitive data. Anyone who violates this will face high penalties. Here, data governance should make a contribution to compliance with official regulations.
Data Governance vs. Data Management
Data governance is a part of data management, albeit an important one. While data governance is about the roles, responsibilities and processes to ensure accountability and ownership of data assets, DAMA defines data management as “an overarching term that describes the processes used to plan, specify, activate, generate, acquire, maintain, use, archive, query, control and delete data.”
Although data management has become a common term for this discipline, it is sometimes also referred to as data resource management or enterprise information management (EIM). Gartner describes EIM as “an integrative discipline for structuring, describing, and managing information assets across organizational and technical boundaries to improve efficiency, promote transparency, and enable business insight.”
Data Governance – Framework
Data governance is best thought of as a set of features, rules, and tools that support an organization’s overarching data management strategy. Such a framework provides companies with a holistic approach to collecting, managing, securing and storing data. To understand what such a framework should encompass, DAMA experts imagine data management as a wheel, with data governance as the hub from which the following ten areas of data management radiate:
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Data architecture: The overall structure of data and data-related resources as an integral part of the enterprise architecture;
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Data modeling and design: analysis, design, construction, testing and maintenance;
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Data storage and operation: Providing and managing structured physical data storage;
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Data security: ensuring data protection, confidentiality and appropriate access;
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Data integration and interoperability:The doorbell rings. I choke on a cough drop in terror. But the pizza subscription is early today. Otherwise he won’t come until around 12:15 p.m. No matter, I’ll take a lunch break now and then get started afterwards.
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Documents and content: Store, protect, index and enable access to data from unstructured sources and make that data available for integration and interoperability with structured data;
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Reference and master data: Managing shared data to reduce redundancies and ensure better data quality through standardized definition and thus a consistent understanding of data;
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Data Warehousing und Business Intelligence (BI): Managing analytical data processing to enable access to decision support data for reporting and analysis;
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Metadata: Collecting, categorizing, maintaining, integrating, controlling, managing and providing metadata;
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Data quality: Define, monitor, maintain data integrity and improve data quality.
When determining a data strategy, each of the above facets of data collection, management, archiving and use should be considered.
The Business Application Research Center (BARC) warns that this cannot be a “big bang initiative”. As a highly complex, ongoing program, data governance runs the risk of those involved losing trust and interest over time. To combat this, BARC recommends starting with a manageable or application-specific prototype project. Findings from the project can then be extended to the entire company.
