Approaches to Assigning Data Ownership and Stewardship

Organizations can take multiple approaches to assigning Data Owners and Data Stewards for enterprise data. In doing so, they need to consider several factors and answer the following questions.

Working with Data Stewards

Question #1:  Should we tie ownership/stewardship to data types?

Accountabilities may be tied to a type of data that may be

  • Master Data
  • Transactional Data
  • Reference Data
  • Metadata
  • Historical Data
  • Temporary Data
  • or other types

Best Practice: Most organizations answer “yes” to this question. Stewards or others who are assigned data-related responsibilities are expected to work with only one or a few types of data rather than all types.

Question #2:  For what data subject areas will we first assign ownership/stewardship?

Information-related or metadata-related accountabilities that focus on Master Data may be tied to different subject areas, such as:

  • Customers
  • Products
  • Locations
  • Organizational hierarchies
  • etc.

Best Practice: Data Governance pilot projects often strive to govern a manageable set of data elements within a single subject area. Accountabilities are assigned to standardize data elements, specify and enforce valid values, and address data quality.

Question #3:  How should we assign ownership/stewardship to data subject areas?

Some organizations assign an Enterprise Data Steward with ultimate accountability for data within a subject area or domain. Others create communities of Data Stewards and others who work with that data. Another approach is to tie accountabilities to a Master Data Management program rather than to stewardship. And still another approach is to assign data-related responsibilities to functional roles rather than to stewards.

Question #4:  At what level of granularity should we assign ownership/stewardship?

Information-related accountabilities may be tied to different levels of granularity of information.

  • Documents
  • Content units (used in documents, web displays, reports, etc.)
  • Data feeds
  • Data records
  • Raw data
    • Domains of data (for example, all data related to Customers)
    • Usage-related collections of data (for example, all fields appearing on a certain report, or all fields included in a compliance mandate such as HIPAA, HMDA, or Sarbanes-Oxley)
    • Specific data entities (for example, within a data feed, an entire a Customer record, including the customer’s ID, name, and all related data)
    • Data attributes (for example, only a certain preference flag within a customer record)

Best Practice: Most organizations getting started with Data Governance and Stewardship feel that assigning all levels of granularity simultaneously is a “boil the ocean” type of mistake. Instead, they choose certain levels of accountability for certain data, then expand scope over time.

Question #5:  Should we tie data ownership/stewardship to processes and data flows?

Some organizations assign just one Data Owner or Data Steward for a data element or subject area. This person is responsible for the data no matter where it appears in an organization. This approach is not feasible for most organizations, however, with complicated data flows.

An alternative is assigning accountabilities for only a few segments in a data flow. One or more Data Stewards or SMEs could be responsible for access control, quality, or typical Master Data responsibilities for specific data within those segments.

Question #6:  Should we tie data ownership/stewardship to compliance and/or usage?

Some organizations assign accountabilities for related sets of data. For example, HIPAA requires protections of personally identifiable information; some organizations put teams in place to locate that data across systems, to specify controls for the information, and to monitor compliance. Likewise, some lending institutions may assign accountabilities to review all data subject to Home Mortgage Disclosure Act (HMDA) compliance.

Read Next:

Dealing With Politics

It’s essential that Data Governance and Stewardship program facilitators avoid being “caught up” in politics. It’s our jobs to acknowledge the realities of the situations we work with, while avoiding taking sides or engaging in behaviors that could be perceived as favoring one set of data stakeholders at the expense of others.

Choosing Governance Models

It’s important to define the organizational structure of your Data Governance program. But before you can do that you have to define your governance model at a higher level. You need to consider what types of decisions your governance bodies will be called upon to...

Starting a Data Governance Program

A successful Data Governance program does not begin with the design of the program! Before you start deciding who goes on what committee, you should be clear about your program’s value statement. You should have developed a roadmap to share with stakeholders. Those...

Goals and Principles for Data Governance

What do you want Data Governance to accomplish?  Regardless of the focus of your program, chances are you hope to accomplish the following universal goals for Data Governance programs: Goal – Enable better decision-making Goal – Reduce operational friction Goal –...

Establishing a Data Governance Office

Most organizations that begin a formal Data Governance and Stewardship effort need a support team to facilitate and coordinate activities of councils, stewards, and stakeholders. This support team may be individual contributors who have been doing this work informally...

Governance and Issue Resolution

One of the three most important jobs of a Data Governance program is to help resolve data-related issues. These may be conflicting data definitions, data usage concerns, or problems with how data is sourced, how it is integrated, how it is protected, or a myriad of...

Defining Data Governance

How you define your program will influence your ability to manage it — to keep all participants on focus, in sync, and striving toward the same goals.

Funding Models: Funding Data Governance

The DGI Data Governance Framework addresses funding two ways: Obtaining funding and support is a phase in the Data Governance Life Cycle Funding is part of one of the components of the framework. What type of funding is needed? Data Governance programs need to...

Focus Areas for Data Governance: Privacy, Compliance, Security

This type of program typically comes into existence because of concerns about Data Information Security controls, or compliance. Compliance, in this context, may refer to regulatory compliance, contractual compliance, or compliance with internal requirements.This...

Focus Areas for Data Governance: Management Alignment

This type of program typically comes into existence when managers find it difficult to make “routine” data-related management decisions because of their potential effect on operations or compliance efforts.Managers may realize they need to come together to make...