Data Governance Practical Application Techniques: Part 1 – Data Governance As A Service


Data Governance has been with us for quite some time now, and for years has regularly been cited as a corporate objective for many companies. While there have been some notable success stories, many companies have found it difficult to get Data Governance to stick. One of the main reasons for this may be that Data Governance is often viewed as a massive undertaking, with a great deal of time and effort needed to agree, define and document data across the enterprise. This notion is emphasized by many vendors, who advance the idea of ‘holistic data governance’. While there is no doubt that full on, end to end data governance can bring many advantages to a company in the areas of revenue generation, cost and efficiency improvement and risk management, there are some techniques that can be used to accrue much of the benefits without a full commitment to enterprise data governance.

There is no single, universally accepted definition of Data Governance. Rather there are many very similar definitions. The Data Governance Institute uses this one:

‘Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise.’

As can be seen, the definition refers to several outcomes desired from Data Governance. And often, companies use this definition or one similar and define a program that will address all of these outcomes. This leads to Data Governance being regarded as ‘all or nothing’, and the Data Governance organization is in the unenviable position of defining how all of these outcomes will be managed across the whole enterprise. This has led to many issues for the organization, ranging from trying to be in too many places at one time, through trying to define interaction points and responsibilities for all facets of Data Governance to managing demand for multiple tasks that are not well understood by the people and projects that are trying to adopt Data Governance.

Some companies have tried to simplify Data Governance by removing that ‘black box’ nature of the Data Governance Organization and deploying  Data Governance As A Service (DGAAS). Technically speaking, it is actually Data Governance As A Series of Services, since the various services are defined as stand-alone but complimentary. These Services break Data Governance into manageable components, and allow for a more detailed understanding of what will be achieved and what will be needed. For example:

  • A Data Governance Advisory Service might allow stakeholders to understand their role in Data Governance and the support they will receive from a Data Governance Organization
  • An Issue Resolution Service might provide support for data issues that cross functional boundaries, and help with investigations into issue root causes
  • A Quality Reporting and Monitoring Service might help determine specifically who will be responsible for the reporting and monitoring of Data Quality and remediation of bad data, and help determine the headcount that will be necessary to deliver that.

Before we get to a more detailed discussion of what the services might be, we should look at some actions that might make it easier to understand how the Services will be defined.

There are three components that have proven successful for the adoption of the DGAAS model. They are:

  • The Operating Model will describe who will produce and who will consume the Services
  • The Data Governance Lifecycle will lay out where the Services will be applied
  • The Data Governance Services themselves will describe what is being done, and the responsibilities of the producers and consumers of the services.

Operating Model

For the Data Governance Service model to be successful, you must first understand who will be providing and who will be consuming the services: i.e. define an operating model. This Operating Model provides the means to prioritize, adopt, execute and oversee Data Governance.  Companies have defined three main functions for this operating model:

The Data Ownership and Stewardship function is comprised of business stakeholders and provides:

  • Accountability for and execution of Data Governance
  • Business rules for enterprise data use
  • Provide business representation and transparency on requirements from respective area.
    • Data Ownership and Meaning
    • Data Quality
    • Data Usage
  • Identify and agree on authoritative data
  • Drive initiatives that provide governed data in support of BU objectives

The Data Governance Administration and Support function owns Data Governance . It is comprised of the Data Governance Organization and:

  • Drives adoption of and provides support for Data Governance across the enterprise
  • Develops and supports Data Governance templates and artifacts to enable Data Governance across the enterprise
  • Provides and supports tools and technology to enable Data Governance e.g.
    • Data Catalog
    • Data Profiling and Quality
  • Provides support for Data Governance operations and services
    • Data Governance
    • Data Quality
    • Issue Resolution support

Finally, the Executive Leadership is comprised of senior executives from Business, Data and IT. This function:

  • Ensures alignment of Data Governance with Corporate and Business Area Strategies
  • Advises on the vision, objectives, priorities, and goals of the enterprise Data Governance capability
  • Secures and conducts oversight of required funding for operations and initiatives
  • Provides clear ownership of data and associated accountabilities
  • Reviews and ratifies Data Governance policies, standards, and major data governance related business initiatives

Data Governance Lifecycle

Next, in order to understand where the services will be applied, we should define a Data Governance lifecycle.  Although it is often regarded as a single capability, Data Governance actually consists of 2 phases: Adoption, which is designed to being the data under governance, and Execution, which is designed to achieve the desired outcomes and monitor the data to ensure that the achievement is sustained. These phases have different objectives and the responsibilities of the participants are different.  Examples of the focus and features of these phases is illustrated below:

Data Governance Services

The actual services themselves will vary by company and what they are trying to achieve. Some reasons for adopting the Service model are:

  • When Data Governance is not being used for all initiatives, but only in some cases (e.g. building a new Master Data Hub)
  • When Data Governance is being retrofitted in legacy systems for a specific purpose (e.g. Regulatory Reporting)
  • When a company has cross-functional data issues that are hard for any functional organization to detect and fix
  • When there is a need to certify data and reports
  • To help determine who will be responsible for Data Quality Measuring and Reporting

Examples of Services that are in use in some companies are:

  • Data Governance Adoption or Advisory Service, which assists with the initial adoption of Data Governance. Activities that may be undertaken as part of this service include:
    • Locate best sources of information, identify data owners and stewards at these locations
    • Facilitate cross-functional discussions where there are conflicts around data meaning and usage
    • Identify potential downstream issues with proposed data changes
    • Identify and help reuse previous definitions of data
    • Document data in the Enterprise Catalog
  • Data Discovery and Profiling, which can help the business understand the contents of their information assets by examining the data contents of the asset and collecting statistics and information about that data. The profiles can then be used to discover business rules and form the basis for quality reporting by providing an initial baseline for the data
  • Data Certification, which provides support for the Certification that the data is fit for purpose, and obeys all rules for areas such as quality, security, accessibility, etc.
  • Training and Communication Services can be used to deliver training and communication to the Data Governance Stakeholders. Communication should be developed and delivered for stakeholders to provide information about Data Governance, their role in Governance and the ongoing measurement of Governance. Specific training may also be developed and delivered to the critical participants in Data Governance, including Data Owners, Stewards, Business Analysts, etc.
  • A Data Quality Measuring Service that performs the actual quality measurements and provides information on quality metrics and thresholds defined by the data owner/steward may be developed. This may be augmented by a Data Quality Monitoring Service that measures and reports data quality metrics on a regular basis, and monitors remediation when the quality slips below standards
  • An Issue Management Service can be developed to assist the business track and resolve data issues. It may consist of providing root-cause analysis on the data issues, and facilitating issue resolution between business owners both upstream and downstream. It also assists the business prioritize issues, and log and track their aging and resolution

Obviously, not all Services described above will be needed for all companies, and there are likely to be other Services that companies will adopt to meet their specific goals. However, the development of Services such as these describes above can help business stakeholders:

  • Gain a better understanding of what Data Governance actually consists of
  • What their part in it will be
  • What they will get out of it
  • How the Data Governance Organization will provide support for Data Governance.

Defining Services such as these laid out above can be effective  in demystifying Data Governance when a company has decided to fully commit to the program, but it can also be effective in showing the value of Data Governance and its supporting organization even if full commitment to Data Governance is not made. For example, a Service such as Issue Resolution could help establish the Data Governance Organization as one that can see across functional data issues and bring resolution to problems that cross functional boundaries. A Data Quality measuring service can be deployed as a stand-alone service to help parts of the business understand and improve the quality of the data. These types of example can help companies understand the value of Data Governance and lead to more proactive adoption.

Data Governance Services do not change the underlying nature of Data Governance, but companies who have adopted this technique have found that it is much easier for them to communicate the nature of and requirements for Data Governance to the stakeholders. Their Data Governance Organizations have also found it easier to manage their own workload when they organize around the delivery of the Services. Taken in tandem, these results make it worthwhile considering Data Governance As A Service.

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