Different Shades of Data Stewardship

Different Shades of Data Stewardship

A comparative study of different stewardship models. Discussing data stewardship with seasoned data governance practitioners can yield different definitions and understandings of what it is

By Shailendra Singh Bisht

A very special thanks to _**_Dr. Nigel Mckelvey(LYIT)**_ for reviewing and sharing insightful thoughts for this article._

Data Stewardship

As defined by DAMA, Data Stewardship is: “The most common label to describe accountability and responsibility for data and processes that ensure effective control and use of data assets. Stewardship can be formalized through job titles and descriptions, or it can be a less formal function driven by people trying to help an organization get value from its data[1].” This definition gives the flexibility that either organization can employ someone with the title of Data Steward who is responsible for his/her respective data domains or this could also be assigned to someone already having good knowledge of data and can help to improve the overall quality of organizational data. This person is responsible for the quality of data and should also manage standard business definition and metadata for critical data elements [4].

As described by (Loshin 2011) Data steward role includes numerous responsibilities and the most critical ones are [4]

1. Supporting the user community: The data steward is accountable for gathering, organizing, and triaging issues and problems with data and timely communicate the impacted users.

2. Managing Standard Business Definition: Recognizing key business terms for resolution and uniform definition, confirming compliance with naming standards and naming conventions.

3. Managing metadata: Develop and approve data definitions, business naming standards, aliases, and authenticating information about the mappings, attributes standard entities, definitions, reference domains, data domains, and code mappings.

4. Managing data quality standards: The steward must participate in the development of data quality rules and data quality standards that must be applied to the data sets that are used and created within a business unit. These standards are to be submitted to the Data Council for review.

data-stewardship data-governance data-science

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