Iterative Data Governance

By Anne Smith

Many organizations are examining the concepts of data governance and planning to implement a data governance program. Some organizations are concerned that a formal enterprise data governance program will take many months to implement and may not deliver tangible business value quickly enough to satisfy leadership. Other organizations may be troubled by the problems reported with large governance programs and would like to implement a more tactically focused data governance process. A new approach that addresses both these sets of concerns could be called “Iterative Data Governance”.

Iterative Data Governance draws its approach from Agile Methods, a process for software development that has become popular within many organizations. Agile methodologies generally promote: a project management process that encourages frequent inspection and adaptation; a leadership philosophy that encourages team work, self-organization and accountability; a set of engineering best practices that allow for rapid delivery of high-quality product; and a business approach that aligns development with customer needs and company goals. Many of these principles can be tailored for a data governance methodology.

One important point to remember is that any data governance program should be planned and supported by the enterprise, and that the strategic goals and objectives should target the enterprise and not one or a few projects. Implementation of these enterprise goals can be accomplished iteratively, but iterations are successful when they are planned as part of a continuum and organized holistically. A scattershot approach to governance is not effective.

Some foundational aspects of data governance programs must be included in an iterative approach to data governance:

  • Development of an active and sustained data governance council – a group that can represent the data management goals and needs of the organization, with authority to make all data-oriented decisions. The council must include an active executive sponsor and the council must have authority over data governance for the enterprise, but it should be a small group (5-7 people maximum). The council’s leader is the Data Governance Practice Manager, usually drawn from the ranks of the subject areas’ chief stewards. Occasionally, the leader of the data management group serves as the Data Governance Practice Manager.
  • Development and training of a core set of chief business data stewards – those individuals responsible for the data and meta data needs of a subject area, across projects. These chief stewards will work with other business representatives (line business stewards and subject matter experts) as necessary for completion of various data governance tasks within projects. These stewards will have responsibility for data-oriented decisions that affect their subject area, and will be expected to collaborate with the rest of the stewards to develop cross-subject-area approaches to data and meta data management, with the approval of the data governance council.
  • Identification of core technologies to support data governance and enable the stewards to work effectively – including a repository for central meta data management and a document / discussion portal for collaboration and communication.
  • Development of a core team of data analysts to support the business stewards – assisting stewards in their function and providing expert data management knowledge transfer.
  • Development and publishing of a core set of data management standards – can be drawn from industry standards such as Dublin Core, ISO 11179, etc. and tailored for the organization.
  • Development and publishing of a core set of forms/documents used by the data governance council and data stewards, including a basic handbook for data stewards.
  • Inclusion of data governance activities in all organizational projects, and refinement of the development and enhancement processes to include data governance tasks.
  • Development and implementation of an iterative program plan for data governance and stewardship, including communication of the iterative data governance approach’s goals, objectives, challenges and essential activities.
  • Education for members of the data governance council and all data stewards; some training will occur at the foundation of the program but most training will occur as each project requires.

These foundational aspects can start small and grow as needed, as long as that growth is managed and these foundational items are sustained actively.

The iterative approach for data governance for projects could include the following steps and points.

  • The data governance council will identify the set of data to be worked on for the project, either by subject area or on data needed for a particular development or enhancement effort. Some projects will focus on gathering and defining meta data (definitions, formats, etc.) while other projects will focus on data quality (data cleansing, data rationalization, etc.) and others will focus on data integration for a data warehouse, development of master data, development of an enterprise model, participation in business process re-engineering, etc.
  • Each iteration of a data governance project should be managed by a chief steward who works with a small team of data stewards to address the data problems to be resolved by this iteration. These stewards will work with other subject matter experts, technical staff and additional business representatives who can provide deeper knowledge of aspects of the data problem(s). However, the stewards have the responsibility for all data governance solutions for this project, with necessary approval from the data governance council.
  • Focus should remain on the identified data goals and issues for this iteration. If an issue leads to discovering additional problems, these new items should be listed separately and would be addressed if the governance council approves the increased scope. The data governance council should remain aware of these new issues and should plan to address them in a subsequent project. For successful iterative data governance, scope must be managed closely and deadlines should be appropriately aggressive, including time to add meta data to the storage facility and produce essential documentation.
  • In large organizations several data stewardship teams may work on data governance projects simultaneously. These teams must be managed by the data governance council, and collaboration and open communication across the teams are essential for success.
  • Data-oriented decisions must be documented and communicated across the organization to reduce data redundancy and to enforce data and meta data standards. Some iterative / agile approaches avoid documentation tasks, but successful data governance requires fairly thorough documentation of the problem, suggested resolutions and chosen solution to reduce re-work for the same problem. Capturing the appropriate meta data (business and technical) in a central meta data storage facility will assist in achieving the goal for data steward-oriented documentation.
  • On a project, the data stewards will review the identified data situation, including the need to create new data, and perform all or many of the following tasks: data and meta data discovery for the appropriate set of data; data and meta data profiling for confirmation of the identified problem, especially for data quality and redundancy issues; participation in the development of a data model for the project; creation of needed business and technical meta data for the data set including ETL algorithms and other business rules that affect the data; participation in loading and validating the meta data in the managed meta data storage; refinement of appropriate business processes based on changes resulting from project activities and goals.
  • Since frequent project / activity meetings occur in agile methodologies, the working data stewardship teams should hold frequent short meetings for collaboration and communication of results, in addition to their working sessions. Also, the data governance council should meet regularly to provide guidance and direction and to resolve issues that require their involvement. Many of these meetings can be short, since they occur frequently. However, communication of results for each meeting is essential so that decisions are not revisited; regular communications can avoid collisions with concurrent efforts.

The services of an expert in data governance concepts and implementation and with experience in implementing iterative data governance can help an organization achieve value from this approach. Many users of the iterative approach to data governance can see benefits in 6-8 months from inception of the foundational activities. Sustaining the iterative approach requires commitment, inclusion of the iterative data governance concepts and activities into all projects and business efforts, and training in governance and the tasks of data stewards. “Big oaks from little acorns grow” can be the motto of an iterative data governance approach.

About the Author

Anne Marie Smith is a leading consultant in Information Management and is a frequent contributor to various IS publications. Anne Marie has over 20 years experience in information management for several corporate entities and has successfully led the development of data resource management departments within corporations and consulting organizations. Anne Marie is active in the local chapter of DAMA and serves on the board of directors of DAMA International, and is an advisor to the DM Forum. She has been an instructor of Management Information Systems (MIS) with Philadelphia, PA area colleges and universities. Anne Marie has taught topics such as: data stewardship and governance, data warehousing, business requirements gathering and analysis, metadata management and metadata strategy, information systems and data warehouse project management. Anne Marie’s areas of consulting expertise include metadata management including data stewardship and governance, information systems planning, systems analysis and design, project management, data warehouse systems assessment and development, information systems process improvement and information resource management/data resource management. Anne Marie holds the degrees Bachelor of Arts and a Master's of Business Administration in Management Information Systems from La Salle University; she has earned a PhD in MIS at Northcentral University. She is a certified logical data and process modeler and holds project management certification. Anne Marie can be reached at

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