Due to the rise of data privacy regulations to compete with the massive volume of consumer data in our competitive market landscape, companies have placed greater significance on the protection and handling of data. With a well-planned data governance framework, companies can have a better-streamlined flow of information throughout the company and can bring better value to the information and data that can bring benefit to the company and its stakeholders.
This article highlights the definition of data governance, its differences with data management, and the importance of a data governance framework in the streamlining process to better understand what’s data governance overall.
What Is Data Governance?
Data governance is an essential element in today's fast-paced and highly competitive economic and corporate environment. Now that organizations have the opportunity to receive massive amounts of diverse internal and external data, they need a streamlined process to maximize the value out of those data, manage security risks, and reduce data management costs.
Data governance is a collection that defines the processes, roles, policies, standards, and metrics for the end-to-end lifecycle of data that ensure the effective and efficient use of data and information for a company’s growth towards its goals and has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.
Data governance ensures that roles related to data are clearly defined and that responsibility and accountability are agreed upon across the management levels. The need for effective data governance increases when data gets stored in various locations and the cloud data governance procedures such as permissions, guidelines, and metadata is inconsistent across databases.
Data Governance Vs Data Management
The information technology industry defines data management as the implementation of strategies to make sure that data is available to the right users at the right time for the right tasks. The idea is that managers have access to only the information they need to make sound decisions. Data governance, on the other hand, is a corporate-level initiative concerned with processes and controls that ensure the proper management of all enterprise data. This includes how different business units are using it and how it is safeguarded from improper use or access by unauthorized individuals.
Data governance is a collection that defines the processes, roles, policies, standards, and metrics while data management is the practice of ensuring that an organization’s data is accurate, relevant, and effective in fulfilling its business objectives and is basically the technical implementation of data governance. This includes activities to maintain data such as data classification, labeling, and proper handling. Data governance, on the other hand, sets the policies on how an organization manages data, and implements and monitors compliance. Data governance without implementation is just documentation.
Data governance is just one part of the overall discipline of data management, though an important one. While data governance is about the roles, responsibilities, and processes for ensuring accountability for and ownership of data assets, data management on the other hand is an umbrella term that describes the processes used to:
1. Acquire
2. Plan
3. Archive
4. Create
5. Enable
6. Control
7. Specify
8. Purge Data
9. Maintain
10. Use
11. Retrieve
Data Governance Framework
Data governance may best be thought of as a function that supports an organization’s overarching data management strategy. Such a framework provides your organization with a holistic approach to collecting, managing, securing, and storing data.
In order to further understand what a framework should cover, imagine data management as a solar system, with data governance as the sun from which the following planets or data management knowledge areas rotate:
1. Data architecture
2. Data Modeling and Design
3. Data Storage and Operations
4. Data Security
5. Data Integration and Interoperability
6. Documents And Content
7. Reference And Master Data
8. Data Warehousing and Business Intelligence
9. Metadata
10. Data Quality
When establishing a strategy, each of the above facets of data collection, management, archiving, and use should be considered.
The Objectives of Data Governance
The goal is to establish the methods, set of responsibilities, and processes to standardize, integrate, protect, and store corporate data. An organization’s key goals when using data governance should be to:
1. Comply with data privacy regulations and data governance policies
2. Manage and minimize the risks
3. Minimize or reduce the costs
4. Establish internal rules for proper data use
5. Improve internal and external communication
6. Increase the value of how valuable the data is
7. Facilitate the administration of all the above
Conclusion
Nowadays, companies and organizations have massive amounts of data about customers, clients, suppliers, patients, employees, and more. Data governance will also make sure that this data is kept secure, compliant, and completely confidential. Data governance also needs to comply with data privacy regulations to avoid misuse of data. For a more in-depth guide on data governance, you can learn from Satori’s Data Governance Guide and learn more about what data governance is in more advanced terms, the principles that go into it, along with its components, and a deeper look into the details of data governance.