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    Home » Big Data » What is Data Governance? Why do organizations need to do data governance?
    Big Data

    What is Data Governance? Why do organizations need to do data governance?

    Data governance is the set of strategies, processes, standards, metrics, and roles that ensure the effective use of data to support an organization in achieving its goals.
    Updated: Jul 10, 2025
    What is Data Governance? Why do organizations need to do data governance?

    With the increasing value of data generated in the era of big data, the problem of its data quality has led to the failure to realize the expected demands of many enterprise projects. Without data governance, more business and technical investment is futile. Data governance is a necessary means to ensure data quality, and strengthening data governance to improve data quality has become an important task for enterprises to enhance their management capabilities.

         Definition of Data Governance

    Data governance is a set of strategies, processes, standards, metrics and roles that ensure the effective use of data to support an organization in achieving its goals. In short, data governance is a framework that helps organizations better manage their data assets.

    It identifies the roles and processes responsible for determining the security and quality of data across the organization. Data governance clarifies who is authorized to take what action on what data using which technology under what circumstances. A well-designed data governance strategy is critical for all organizations.Data determines the future growth of an organization, but explosive data growth has also left most organizations facing a number of problems with data management:

    1. Data quality is not up to scratch

    Common data quality issues under big data include data inconsistency, data redundancy and duplication, data that cannot be matched, and insufficient data accuracy, which usually cost organizations additional costs.

    2. Poor data interoperability and lack of unified master data

    Enterprise development to a certain stage, the emergence of multiple divisions, each division has its own data, the data between the divisions are often stored separately, each defined. Each division's data is like an island that can not (or extremely difficult) and other data within the enterprise to connect and interact.

    3. Data security is difficult to ensure

    Most of the sensitive and important data of the enterprise units are concentrated in the application system, and data leakage occurs every year at home and abroad, and the leaked data is counted in hundreds of millions, so how to protect the data from being leaked and illegally accessed is a very critical issue.

    4. Data value to be released

    Many enterprise units have not established an effective mode of managing and applying data in the process of digital transformation, resulting in the efficiency of work has not been significantly improved, the value of data can not be effectively released, and ultimately the enterprise itself is difficult to adapt to the constant changes in the market, the industry, and technology, and even more so to make the management of data assets a continuous and dynamic process.

    Benefits of data governance for enterprises

    1. Improve data quality

    Data governance requires the establishment of rules and standards to meet the business functions of an enterprise. Data governance activities must be strictly enforced based on following rules and processes. As duplicate data continues to be reduced, data inconsistency is reduced, and the quality of source data continues to improve, the data cleansing costs of the enterprise will continue to shrink, and at the same time, data cost reduction can be realized by promoting standard procedures among business departments.

    2. Improve decision-making ability

    Enterprise demand for trusted data is on a spiraling upward trend. Data governance strategies and processes establish the necessary framework for transforming data into business value. A data governance framework helps organizations manage data more effectively. Examples include providing consistent definitions for data that is dispersed across business units, establishing an enterprise data management unit, and monitoring data quality. Data governance coordinates the understanding of data by developing common terminology that is applied consistently across the organization.

    3. Reduce Potential Risks

    Current businesses are usually aggressive with data and information. In order to conduct business, enterprises usually collect, analyze, and use data information about customers, products, and changes in the business environment in a number of key areas, but accidental data leakage will undoubtedly have a large negative impact on their business.

    Many organizations lose sensitive data due to lack of proper data governance strategies. Effective data governance can provide comprehensive support for data access control, assessment, risk management, clarification of data stakeholders, establishment of decision-making power allocation mechanism, etc., which will help protect sensitive data.

    4. Improve Security Regulation

    Security regulation is one of the core areas of data governance, which is related to data privacy, access management, access management, information security control, and compliance and enforcement of norms and contracts. On the one hand, data governance should be conducted within the legal framework of the business, and on the other hand, data governance policies and rules should be formulated in a way that is consistent with relevant government and industry standards.

    Data management efforts need to be shared across all departments throughout the organization, and risks due to non-compliance with regulations and norms can be mitigated through effective data governance. The application of data standards across key business and cross-business functions creates a harmonized processing and analysis environment for security regulation.

    management Data governance framework
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