Difference between Data Management and Data Governance
Last Updated :
02 Aug, 2024
Data Management and Data Governance are both critical aspects of handling data within organizations, but they focus on different facets of the data lifecycle and have distinct roles. Data Management involves the comprehensive administration of data throughout its lifecycle, encompassing acquisition, storage, processing, and disposal. It emphasizes ensuring data is accurate, available, and accessible for operational and analytical purposes. Data Governance, on the other hand, refers to the framework of policies, processes, and standards that ensure data is managed effectively and responsibly. It focuses on establishing rules for data access, usage, quality, and security to ensure data is trustworthy, compliant with regulations, and aligned with organizational objectives.
What is Data management?
Data management is the creation and execution of the policies, architecture, and procedures that manage the complete data lifecycle needs of a Business. It is important to have these policies and procedures in place for analyzing complex, big data. When data is treated as a significant company profit, it needs to be managed. Data management includes many different types of data projects and one of those is data governance.
- Data preparation: It is a process of cleaning the raw data to analyze the accuracy. This censorious first step is sometimes missed in the flurry of analysis and report, for this organization makes the wrong decision with the wrong data.
- Data Pipeline: It is a set of manners that extract data from various resources. It’s an automatic process. Its main work is data extraction, transforming and loading data in the data warehouse.
- Data catalogues: It is a complete map That helps to find out and track the data in data warehouse and also managing the metadata.
- Data warehouse: It provides a clear path for data analysis by merging all the data sources.
- Data governance: It helps define policies maintaining data security as well as data observance.
- Data architecture: it would be the formal structure for managing the data flow.
What is Data governance?
Data Governance establishes procedures and responsibilities that ensure the quality and security of data used in a business or organization. It also helps the organization to make the right decision. It’s also a policy that controls the data usage. A well-designed data governance program includes a governance team, a guiding committee that acts as the governing body, and a group of data stewards.
- Data quality: it is the base of data source management. Data governance needs high-quality and strong data. Having accurate, complete and reliable data is the foundation of any data-driven firm.
- Data security: it defines and labeling data by the level of risk they have. It maintains secure access between user interaction and security
- Data stewardship: it guides monitor how to use data sources and helps to provide high-quality data to business users that is easily accessible.
- Data transparency: it is specially used for data security means the person who uses the organization's data utilises the data for good reasons and how they use it Business users easily find out where their data comes from.
Data Management Vs Data Governance
Here are the following difference between Data Management and Data Governance:
Aspect | DATA MANAGEMENT | DATA GOVERNANCE |
|---|
Definition | Data Management refers to the methods how the data is organized. | Data governance refers to policies, rules and controls to governing the data and managing data quality. |
Focus | Operational handling and processing of data. | Strategic oversight and policy development. |
Scope | Includes data storage, integration, and quality | Includes policy creation, compliance, and stewardship. |
Refers To | Refers to collecting, organizing, protecting, processing, sorting and maintaining data. | Refers to practice, establishing process and theories. |
Focus | It focuses in making that are more quality and more valuable. | It focuses on reliability and safety of data. |
Method | It is a logistic method of how to organized the data properly. | It is a method of action to achieved the high quality data. |
Objective | It is logistical and focused on technology. | It is philosophical focused on an overall business strategy. |
Key Activities | Data integration, data quality management, data security | Policy formulation, compliance monitoring, data ownership |
Tools & Techniques | Data management systems, data quality tools | Governance frameworks, compliance management tools |
Conclusion
In conclusion, Data Management and Data Governance play complementary yet distinct roles in the effective handling of data within organizations:
- Data Management is primarily concerned with the operational aspects of handling data throughout its lifecycle, ensuring data quality, accessibility, and usability for various business functions.
- Data Governance focuses on establishing policies, standards, and controls to ensure data is managed securely, ethically, and in compliance with regulations. It provides the governance framework that guides data management practices and supports organizational objectives.
Together, Data Management and Data Governance form a cohesive approach to maximize the value of data assets while ensuring they are secure, compliant, and aligned with business goals. Their integration is essential for organizations aiming to leverage data as a strategic asset while mitigating risks associated with data misuse or mishandling.
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