Data stewardship and data governance are important for any organization that wants to derive the maximum value from its data. As more companies realize the potential of data, these two terms are becoming increasingly popular. There are also specific roles and responsibilities that are being established by companies to facilitate data management.
What is data governance?
Data governance is a data management concept that refers to the set of procedures, roles, policies and rules that govern data. It can be used at a macro level by governments to manage the flow of data across borders or at a micro level by corporations to ensure their data is consistent, secure, verified and accessible. It is also used to define ownership and accountability of data assets, data access management and data quality improvement.
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Data governance is a major component of the overarching data management strategy of an organization. It plays an important role in minimizing risk, implementing compliance requirements, increasing the value of data, improving external and internal communication, and optimizing data workflows.
What is data stewardship?
Data stewardship is the implementation of the procedures, roles, policies and rules set by the data governance framework. This includes people, technology and processes. Data stewards or a team of data stewards are tasked with the responsibility of protecting data assets of the entire organization, department, business unit or a small set of data. They are also tasked with the implementation of data governance initiatives, improving the adoption of data policies and procedures, and ensuring users are held accountable for the data in their care.
What are the similarities and differences between data stewardship and data governance?
As data stewardship is effectively a branch of data governance, they share some common goals of protecting data, making it more manageable and getting the maximum value from it. The ultimate goal of data governance and data stewardship is to have fully governed data assets.
Although these two terms are used interchangeably, there are distinct differences. While data governance deals with policies, processes and procedures, data stewardship is only concerned with the procedures. This means that data stewards are not responsible for creating or writing policies or processes, their job is to interpret and implement them on a day-to-day basis. This requires data stewards to have technical familiarity with the data and the systems that use the data, and business acumen to understand integration of data with business processes and outcomes.
Data stewardship best practices
Encourage adoption of data governance
Data stewards must be helpful and accessible to data users to encourage them to adopt data governance. Rather than enforcing the data governance policies and processes, data stewards should focus on highlighting the value of following these policies and processes for the data users and for the organization.
Creating pathways where data users can communicate their problems or ask questions to data stewards will encourage the adoption of data governance. This will also make it easier for the data stewards to maintain the quality of data as they will receive employee and customer feedback.
Regularly verify data quality
Data goes through various stages through the data lifecycle as it gets used for various processes. The quality of data at all stages is important to ensure it is good enough for its intended use. A good data stewardship model ensures that data quality is maintained throughout the data lifecycle. Data stewards can ensure the quality of data by regularly verifying data. In most cases, the organization will need to provide data stewards with training and tools required to verify data.
Establish a data stewardship committee
The goal of a data stewardship committee would be to guide the overall data stewardship objectives of the organization. It will allow data stewards to collaborate and join forces to help accelerate the implementation of data stewardship and tackle issues that require cross-functional effort. The committee can also propose changes and modifications to the data governance model to help make data policies and procedures more transparent to the data users.