Data Quality, Data Stewardship, and Data Governance – How Are They Related?

3 minute read

The data management field is full of terms and buzzwords, and people often have different interpretations about what those terms really mean. Activities in each data management area can overlap, adding to the confusion.

Data quality, data stewardship, and data governance are all required to implement a thorough data management program. Though the end goals of people practicing in each area are similar, and they all relate to an organization’s data assets, the focus at each step is decidedly different.

Here’s an overview of data management components.


Data Quality

Data quality is the starting point. A company can’t advance to a serious data stewardship or data governance implementation unless they’ve addressed data quality, which requires some tools to manipulate and reformat data. Companies use a wide variety of tools to improve the quality of their data from general-purpose programs like spreadsheets and text editors to specialized solutions, which Firstlogic provides.

In the data quality phase, technicians mainly concern themselves with the mechanics of getting data into a state where their information is concise, consistent, and unambiguous. Data quality professionals change the data. A company might, for example, engage in efforts to standardize, combine, and update customer postal addresses. Tasks would include ensuring the addresses meet US Postal Service standards for format and abbreviations. Verifying that addresses are valid USPS delivery points would be included in this effort, as would updating the addresses of customers who moved.

Data quality may also involve file reformatting. Within an organization, some data files may list first and last names in separate fields, while other data sources store names a single field. Technicians working on data quality projects would strive to reformat the data, so names always appeared in the same style. However, they wouldn’t establish enterprise-wide field name conventions or be responsible for making sure others in the organization constructed their files in the same fashion. Those activities are the domains of data stewardship and data governance.


Data Stewardship

Data stewardship relates to an organization’s data asset management and oversight activities. Data stewards focus on tactical coordination and implementation projects. They are often the connection between the IT department and business units. Data stewards leverage skills such as programming and data profiling to carry out data usage and security policies.

Since data stewardship involves data accuracy and availability, data stewards must have access to tools that allow them to inspect, track, and view data. Top companies rely on SAP’s Information Steward software to achieve the necessary levels of visualization and monitoring.

Data stewardship programs ensure organizations have complete data documentation, enforce data policies, and help companies follow data-related regulations.


Data Governance

Data governance is where organizations define data rules, policies, and processes. These items are established by data owners, who are also defined by the practice of data governance. The authority to dictate all the rules, data ownership, and policies enforced by the data stewards comes from data governance. This authority ensures an organization maintains an environment that provides for data usability, quality, and policy compliance.

Data quality is part of data governance, but data governance programs do not describe details like file formats or match/merge schemes. With a data governance program, companies formalize the ways they intend to use their data and identify data-related goals and objectives. The journey to realizing those objectives flows through data quality and data stewardship.


Data Management is a Must-Have

Almost all organizations are now driven by data. They rely on data to make critical business decisions and to enable their transition into digital entities that leverage the power of artificial intelligence and machine learning. This is why data quality, data stewardship, and data governance are so important. Data must be accurate, complete, and accessible to decision-makers within the business.

Companies have recognized that data agility is necessary for them to compete. Making sure data is correct and complete is the job of people working in data quality, data stewardship, and data governance programs. The level of detail and the scope of data management initiatives will vary from one organization to the next, but nearly all companies will incorporate data intentions into their strategic plans.