Executive summary.

The terms "data governance" and its close partner "data stewardship" are often thrown around within organizations today, but rarely with a common definition that triggers action.

Forty years ago, businesses employed people to make products and deliver services with highly tangible and visible processes and outcomes. Several sets of engineers or business analysts with years of tacit process knowledge cross-checked every business form before the information could cause any problem or error. In the last 40 years, however, enterprises have mixed complex information systems with refined processes and automation, employing people to make and manage things that are largely intangible, such as data. This shift has created a challenge for data governance and stewardship. Enterprises would benefit from asking, “Are we giving data the attention we once applied to waste factors, safety stock, corporate risk and customer service?” The answer for most is no. Data governance is the cycle of defining attributes necessary to maintain order, efficiency and control of the data in information systems. Data stewardship is the process of leveraging the attributes defined by data governance to maintain order, efficiency and control of the data. Every large enterprise around the world is currently grappling with these concepts, because they are the next stage-gate for enterprise performance in the information age. Downstream effects of improved data governance and stewardship are reduced cycle times, improved transactional readiness, more trustworthy insight from analytics, greater business agility and leaner, faster and better operations.

Data Governance vs. Data Stewardship

The chief difference between governance and stewardship is that governance is a broad term defining the overall, big picture strategy of data management, whereas stewardship refers to the specific measures taken to reinforce that big picture strategy. If the two terms were nesting dolls, stewardship would be found nestled inside the shell of governance.

Moving the needle on data governance.

Establishing the best start for your organization involves an understanding of maturity and the data that is most critical.

Determining maturity

Analysts and professional services organizations offer a lot of clever definitions of data governance, but those rarely provide a picture of what data governance should “look like” to an organization. The picture is often more important than the definition because some elements of the picture may already exist within an enterprise and a simple definition can’t account for their presence.

The "picture" of data governance is best represented as a matrix, where the three basic components are aligned to particular business objectives:

  • an organization to accommodate data events
  • a set of processes to articulate data-centric policy
  • a body of business execution knowledge essential to providing context to data management routines

If an enterprise has all three of these components, then it is engaged in data governance. The degree of balance in a data governance program is derived from order, efficiency and control. These elements are an important measure of balance because data governance can be the flashpoint for common enterprise dilemmas, such as standardization vs. adaptation in operations and consequently data.

A simple exercise in painting the picture of data governance for your company involves laying out the components of organization, processes and business execution knowledge and reviewing what your company has or doesn’t have in those areas. Layer on top of those three components the objectives of order, efficiency and control to define the purpose of actions your company is taking in each area (see figure below).


Govern the data that matters.

Understand, define and agree on the the types of data and the greatest areas of business value.

There are a number of great business reasons to establish data governance, but not all data warrants the same level of rigor. The value of governing a specific field of data is established by decisions on two levels:

1. How closely does governing the field fit the primary mission of the governance program (i.e., the primary business driver)?

2. Will governing the field provide benefits to analytics, operations or compliance within the primary mission of the governance program?

ERP systems are overwhelmed with data that provides no clear business benefit, and in some cases, the data comprises artifacts from business activities decommissioned long ago. Successful organizations look at data governance with a cautious eye to governing too much at once. Keep the business drivers for governing data tight and the threshold for what to govern high, and you will save yourself some time at the white board.

Keeping a high threshold for what an organization needs to govern is a healthy way to start. The organization involved in data governance, presumably the data management organization, has to become proficient in making tough calls of what to govern, when to govern and how to govern. Keeping a high standard for what data to govern would allow this organization to grow into reasonable expectations and develop both processes and business execution knowledge that are crucial to implementing systems and rolling out improved data stewardship.

The value of specific data fields evolves over time as business operations and regulatory climate evolve. Maintaining a high threshold for what an organization decides to govern will pave the way for an iterative or incremental approach to the data governance cycle and return high value in the long run.

There are many types of data worthy of governance within an organization, including enterprise reporting, transactional data, reference data (sometimes referred to as structural data) and supplemental master data (sometimes referred to as conditional data). However, it is master data that is often at the heart of it all, exposing vulnerabilities in operations, analytics and compliance on a regular basis. The first step in getting an organization behind a data governance movement is clarifying the differences in types of data and generating feedback/consensus on the area(s) of need. Master data governance offers the greatest number of avenues to generate results, so this paper will focus on data governance for master data.

Scoping your program

Scoping out the endeavor of bringing data governance to your organization is largely a communication and discovery exercise. Data governance is a discipline that requires a team to talk about data on a regular basis.

However, the skills necessary to get a data governance program off the ground are non-technical. The components in the previous chart require “soft skill” activities to provide definition, organization and policy development. Recruit team members who are skilled in business analysis, communication and planning to review these areas for the level of need and value. Stay focused on building a unique picture of data governance that your company needs. That picture will likely be a snapshot of business excellence, and expectations you set must be aligned with the business initiatives ahead.

Start small.

Companies in the early process of defining data governance, either for a new domain or for the company as a whole, need to stay focused on delivering value with analyst skills that the team already possesses. Therefore, the first iteration of data governance and the tools used for establishing stewardship should be low on the technology spectrum.

  • Consider your plan for requesting incremental solution funding
  • Understand that your first iterations of data governance will not be set in stone
  • Don't go overboard in purchasing tech

The main reasons for this are as follows:

  • Requesting incremental tool funding to deliver a solution to the same team that delivered the study is far easier than asking for head count plus a large solution license.
  • First iterations of data governance will not be delivered with functional specs etched in stone. Teams should expect a revision cycle that mirrors the rate of organizational learning.
  • Having a tool limitation during early development can provide a natural guardrail, preventing the team from venturing too far from the business’s ability to keep pace. Consulting an expert governance solution architect may be helpful in balancing this decision.
The team positioned to support the data governance cycle needs to be able to recognize the critical inputs for processes and business execution knowledge referenced along the way. These inputs include data standards, governance rules, ownership definitions and data process diagrams. Each set of inputs provides a layer to the picture of data governance, and significant portions of this body of knowledge facilitate decision-making around data governance. Together, these components provide the key decision model for successful data governance choices and follow through.

Information Value Management is particularly well-suited for new data governance programs because of it's robust data governance, policy and stewardship management capabilities.

David Woods, Partner

Set your data governance gameplan in motion

Getting the momentum going is often the most challenging part.

The cycle of data governance can sometimes be a challenging process to set in motion. The concept of data governance doesn’t come naturally to business teams that are frustrated with data, and IT advocates typically step into the picture armed with data quality reports that imply inadequate governance.

The best way to start educating an organization about data governance is by focusing on the business objectives ahead, rather than current or past failures. This is best executed when some background work could be referenced anecdotally as a weak point in operations, analytics or compliance. The data governance cycle is iterative, and a set script may be hard to stick to, as variations in the sequence are expected among projects. Organizations should focus on two levels when initiating the data governance cycle: master data management need and business case.

The master data management need is composed of knowing the answers to the following questions:

  • What is the primary driver for data governance?
  • What is the primary domain or process area?
  • What is the organizational alignment to support it?
  • Who are the stakeholders and sponsor?

The business case does not always focus on an ROI calculation. Heavy calculations of ROI can be distracting and cause inflation of expectations and project scope. Organizations in the early stages of building data governance competencies should take manageable chunks of opportunity with obvious rewards. These obvious rewards are the three objectives discussed earlier: order, efficiency and control.

Eventually, the business case will require all of the following to be addressed at a summary level:

  • Needs Analysis
  • Identification of Stakeholders
  • Perception Analysis
  • Value Proposition
By focusing on master data management need and business case, teams will be able to easily distill and promote essential project elements such as a charter or mission statement within the organization. Either track is a legitimate starting point to initiate the data governance cycle

How to build a business case.

Launch your data strategy successfully using our tips and tricks for building a business case for data governance.

Learn More


Managing information with a level of discipline requires a cultural shift for most companies. Therefore, processes like data governance do not move through an organization like a tidal wave of activity.

Data governance is an ongoing workstream of data management that is built over time with both tactical and long-term initiatives. Furthermore, the picture of data governance is largely unique to each company. Early in the process of proving out data governance, there are strong merits to using tools and techniques that enable analysts to quickly validate the cycle. Once a data governance program is well accepted, the data management organization can look into implementing enterprise-wide capabilities in areas that meet the necessary requirements. Improving data governance and stewardship practices across an enterprise can lead to significant gains in business performance and agility.


DATUM drives decision integrity across any enterprise, empowering organizationsto discover the right data and make the right decisions faster.

By focusing on what data matters and why, DATUM's proven data governance and stewardship platform, Information Value Management®, delivers business value insights. Today, Fortune 500's trust DATUM as the data governance system of record to improve operational efficiency, deliver greater analytical insights and simplify compliance and regulatory reporting.

DATUM was named a Leader in The Forrester Wave™: Data Governance, Stewardship and Discovery Providers 2017 and in Gartner's 2017 Metadata Management Magic Quadrant. DATUM has also received top score in Bloor Research Data Governance Market Update report. Learn more about DATUM here.

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will crump


Will Crump is the CEO of DATUM. He is best known for the strategic direction that has resulted in 6 years of successive double and triple digit growth. His winning drive is applied outside the office in competitive sailing competitions.

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