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Data Governance is a rather capacious term that includes a strategy for managing the availability, usability, compliance with standards, consistency, integrity and security of data in organizations. Data management purpose is to ensure the effective use of corporate data. The main challenge is to determine how, where, why and under what circumstances data is collected and stored.

Digital transformation is happening very fast. This entails the need for new cybersecurity solutions and capabilities. At the moment, data security is an absolute priority. Companies that actively work with data are interested in finding ways to prevent data leakage and improve their operations.

Often Data Governance is used as a synonym for Data Management and Master Data Management. However, all these 3 concepts have a fundamental difference.

Data Management is the process of managing the full data cycle within an organization, i.e. the implementation of data management according to a specific strategy.

Data Governance is the core component of Data Management and is primarily focused on documentation, ownership, access delegation, data retrieval methods, regulatory compliance, and data security measures. Data Governance forms the data management strategy.

Master Data Management is the process of managing major data assets in order to identify key data entities (suppliers, stakeholders, customers, etc.).

Data Governance can also define requirements such as a Master Data Management model, assignment of roles and responsibilities for data creation, definition of data retention policies, data curation, and data access control. This, in turn, affects the effectiveness of Master Data Management.

The absence of an effective Data Governance program can lead to various errors and problems with data, inaccuracies in the system, and, as a result, negatively affect the company’s activities. For example, a registered customer may have a different name in different departments of the company. This leads to problematic data integration, data integrity issues, incorrect business intelligence, and inaccurate reporting.

Tasks and goals of Data Governance:

• Ensuring that data is used and managed appropriately to prevent possible data errors and misuse of confidential information. Also, Data Governance maintains a balance between data privacy and data collection practices;
• Fragmentation of data warehouses (data repositories that are organized and segmented by individual departments, sectors, business units, etc.). Such repositories should be isolated from others to prevent glut;
• Regulatory compliance, compliance with data protection regulations and initiatives. Poor data management indirectly affects compliance. This can lead to serious problems and security incidents, including data leaks;
• Ensuring effective management of information assets of the enterprise.

Benefits of Data Governance:

• Improving data management processes;
• Making business decisions as a result of gaining access to reliable data;
• Reduced data management and data monetization costs by standardizing, filtering and classifying valuable data from collected and generated data;
• Higher level of data understanding for all employees, specialists, customers and business units;
• Higher level of data quality;
• Consistent and accurate data overview about customers, competitors and other objects;
• Increasing staff efficiency;
• Data security.