A data steward — a subject matter expert responsible for a group of data or data domain — typically ensures that their organization defines business terms, creates and maintains data quality rules, and applies data usage consistently and accurately across all systems, applications, and reports.
In some organizations, data stewards may also be responsible for access governance — or granting approval for users to access data sets. They’re often responsible for lines of business in their organization, as well as overseeing data custodians, business stakeholders, or operations team members.
While the role of the data steward can vary widely across different companies and industries, their core responsibilities remain fairly consistent. Here are some best practices for how data stewards can perform most effectively in their role.
1. Learn your data responsibilities
Organizations may choose data stewards to report into the business unit — especially if they have subject matter expertise on the data domain. Alternatively, data stewards may be hired to report in a centralized data function.
Regardless of the organizational hierarchy, all data stewards need to learn what processes they need to adhere to in order to maintain the integrity of the company’s data. If none of these processes or procedures already exist, it’s up to the data steward to create them to ensure that the data is properly maintained.
Creating proper processes involves knowing and identifying:
- where the organization’s data exists and is stored
- who has access to the data — and the purpose of their usage
- which systems are downstream consumers of the data
- which business and risk processes are impacted by the data
- the definition and data quality standards around the organization’s data
2. Communicate and collaborate
A data steward should never work in a silo. They must partner with lines of business — plus technology, operations, and risk and compliance teams — on the proper understanding and usage of the company’s data.
This requires frequent and thorough communication on the proper definition of the data elements. It also may also include documenting all variations of the definition — and striving to standardize a single working definition across the organization.
If the data steward works in a department that also serves as a provider of the data, they may need to communicate the quality of the data. Therefore, it’s crucial that they collaborate to ensure that data elements are used properly throughout the company.
3. Own your data domain
Data stewards must work diligently on a daily basis to create an initial set of definitions for the data elements they govern, standardizing them across the enterprise and carefully documenting all exceptions where they might not apply.
After that, data stewards need to classify the data elements and tag them appropriately for proper contextual usage. Take for example mapping the “personally identifiable” classification to each data element (if applicable). Is it a sensitive, restricted, or confidential element? Should this element only be used in financial transaction calculations?
These types of metadata will help the data analyst understand how to apply the data in different business scenarios. The more business and operational metadata that can be identified and documented in a central repository, the better. This context enables business users to use and trust the data.
4. Understand how data is being used
Identifying how data is used can be tricky, especially in large, sprawling organizations that have complex systems and downstream applications. For one thing, multiple copies of the data may be stored without the data steward’s knowledge. As much as possible, however, it’s important that data stewards identify the lineage of the data, back to front, leveraging technology partners.
Knowing where the data originates helps identify the main sources of data quality areas. Identifying controls at data points where data quality is poor can reduce risk.
In addition, the data steward should know which business areas have a need for the data and may require multiple contextual, specific definitions.
5. Remediate data quality issues
Data stewards oversee data from cradle to grave. Starting at the consumption or ingestion point, the data steward is familiar with the original quality of the data — especially if it comes from a third-party vendor.
Data stewards can also help define data quality rules from the producer of the data set — if the data set is created internally. They can help document data quality rules that are necessary for the data users. Often, these rules will differ for the producer and the consumer.
Ultimately, the data steward works between different groups to help set expectations on data remediation — alongside technology and operations teams.
How BigID Helps Data Stewards
BigID’s data intelligence platform combines a foundation of data discovery with apps to take action – enabling organizations to ultimately get more value from their data.
With BigID, data stewards can:
- automatically discover, catalog, and and map all data across the enterprise for better governance
- leverage ML & AI for deep data insight
- classify personal, sensitive, and regulated data for compliance and reporting
- define and tag data for purpose of use
- take action on duplicate, similar, and redundant data
- apply data retention and data remediation workflows to manage end-to-end data lifecycle management
Request a demo to learn how BigID empowers data management teams to work collaboratively across the business — and technology teams to find, remediate, and protect data.