How to Influence Data Quality Through Data Stewardship

摘要： To get value from data, data stewards must understand business requirements and apply them. When business ambiguity arises about best serving data stakeholders, data stewards need to know how to find out this information and with whom to speak. Then these data trustees influence Data Quality for the better by aligning fit for purpose with business needs.

▲圖片標題(來源：Michelle Knight)

Data stewards understand business standards’ frameworks when taking good care of data assets.

Data Governance, either formal or a non-invasive, reflects these structures and provides context and direction to these frameworks. When a data steward misunderstands the business framework and misapplies Data Governance, Data Quality suffers. Just as a martial arts practitioner in either Kung Fu, Karate, Capoeira, or Neo-Bartitsu needs to understand its concepts and context to best an opponent, data stewards should follow the rules and concepts making data fit for purpose.

In a rapidly changing marketplace, a company’s business specifications vary, especially around Data Quality. Objective Data Quality measurements and thresholds differ based on opportunities and threats. Within this reality, data stewards need to have flexibility with the business standards, while still keeping the data assets, generated, integral to the way the business proceeds.

Describe Data Steward Roles

A company needs to know what data stewards’ roles will do to meet business requirements and enhance Data Quality. In general, data stewards maintain data throughout its life cycle, according to business requirements and work with data owners. However, how they vary depending on a company’s Data Strategy and Data Governance implementations. One Data Stewardship role in one company does not necessarily translate to another.

For example, the Earth Science Information Partners (ESIP) wanted to foster collaboration between geoscientists using a FAIR framework (Making data findable accessible, interoperable, and usable). Towards that end, ESIP needed data stewards to create data citation guidelines and uniform metrics that could be used, by scientists, to find information across multiple earth science data repositories. The data stewards need to have expertise in geoscience, they required technical and librarian like knowledge to format and organize data and metadata.

Freddie Mac, on the other hand, aimed to achieve Data Stewardship credibility within a Ready (laying the groundwork), Set (identifying and working with stewards and stakeholders, and Go (do the Data Governance program as agreed) framework. Data stewards at Freddie Mac needed to establish and maintain effective relationships and connections with data owners, to consult on metrics making data usable and meeting these key performance indicators (KPI). A data steward at Freddie Mac needed to be an empathetic, business subject matter expert who understood the company culture and had excellent interpersonal skills.

ESIP and Freddie Mac’s data stewards overlapped in getting high Data Quality value to their users by cleaning that data and defining rules and policies, as part of the team. However, their specific goals and frameworks required very different data stewardship roles to meet stakeholder needs.

詳見全文： dataversity

若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： 　　　Big Data In Finance