Data Governance Defined
Data governance is one of those topics that many people agree to talk about, explore, and even implement. However, when you ask for a definition, you get many different answers. Often you will get a sort of “I know it when I see it” reply.
This answer is not conducive to success. Businesses and organizations prefer to deal with topics that offer solutions to problem or exploit opportunities. Part of the bad reputation of internal IT departments is due to the perception of fad-hopping. Repositories, data warehousing, and information engineering are all topics that went through a bit of hashing about before the dust cleared. It goes without saying that all three developed perceptions of ‘failure to deliver’ at the CXO level. Often the success stories of a particular topic were intertwined with a business project, and discerning the exact role of the technology was difficult. The definition of the technology often varies across success stories as well. Therefore EDJ canvassed a range of individuals. We asked prominent consultants in the data governance universe, corporate managers who are implementing or managing data governance programs, and vendors who offer data governance products and services for a definition (less than 150 words) of data governance. Before proceeding with the analysis of the various definitions, please take the time to review them. The responses were edited to 150 words or less and reviewed by the contributors after editing.
They are listed below.
Rob Seiner – Publisher of TDan and an independent consultant.
“Data governance is the execution and enforcement of authority over the management of data and data-related processes.” Gwen Thomas – President of The Data Governance Institute, LLC.
“What is data governance? In short, it’s the ‘rules of engagement’ plus the actual rules and processes we all agree to abide by for our data-related efforts. Why do we need to include ‘rules of engagement?’ Ultimately, don’t we want to just make rules and enforce them? Perhaps, but managing data is complicated. Some situations are black-and-white, but others are gray areas. With many decisions to be made and many stakeholders for every decision, we need to start by establishing clear authority levels for a variety of data-related scenarios. Then we can get on with the work of making rules, enforcing them, and resolving conflicts. And so, my formal definition of data governance is “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
Jonathan G. Geiger – Executive Vice President, Intelligent Solutions, Inc.
“Data governance recognizes that data is an important enterprise asset and applies the same rigor to managing this asset is it does for any other asset. A starting point is the establishment of a governing body with overall responsibility for establishing and enforcing policies concerning data. These policies dictate how the asset will be managed and the associated roles and responsibilities. Two of the most critical responsibilities are stewardship and custodianship. Data stewards are assigned to specific sets of data (e.g., customer, product), and they are responsible for establishing definitions and business rules (which are reflected in the business data model), and for the acquisition, maintenance, use, and disposal of that data. Information Technology is the data custodian and is responsible for creating, maintaining, and applying the data models and for managing the system for managing information about the data (“metadata”) and for the systems that electronically handle that data.”
Michelle Koch – Sallie Mae
“At Sallie Mae, Data Governance is ‘solving boundary-spanning issues by pulling together the pieces of the data puzzle’. The key components of our program include resolving data issues using a horizontal perspective of the organization and focusing on the major “pain points” for our business areas. We accomplish this through our Stewardship Council that is comprised of representatives from each of our lines of business so we are assured of enterprise representation and input during issue resolution. Our success to date has been achieved by focusing on what matters most to the business — their major ‘pain points’. Our Data Governance Program is established within our Enterprise Data Management Strategy and focuses on 1) proactively creating and aligning data rules (processes and procedures); 2) reacting to data issues; and 3) serving the interests of the data stakeholders through ongoing support.”
DataFlux (a SAS Company)
“Data governance is a combination of people, processes and technology. A data governance program is an institutional recognition of the importance of high-quality data and a practical commitment to establishing and maintaining this quality throughout the enterprise. This is achieved by having the right technology integrated into your data infrastructure, the right picture of the flow of your data through the organization, and the right business rules and data standardization processes working with that technology. But most importantly, data governance is an organization-wide commitment to data quality, with data stewardship recognized as an essential business role. When an organization has made a commitment to data governance and undertaken to analyze, improve and control its enterprise data, the result is the achievement of integrated, unified, and standardized data that can be used to streamline operations and increase efficiency.”
Space prevents us from exploring every single available definition. However, we can see that a good definition of data governance (for YOUR organization) requires a few constant components.
- It must be expressed as a business program or process. (Our research is indicating this to be a HUGE success factor which we will disclose in a future issue)
- It must encompass cross functional definition of policies, roles for people, and supporting technology to ensure that data is managed correctly.
- The rules must be enforced. This implies measurement, monitoring and accountability. This is most likely the largest area of resistance. But it must be made clear – if you cannot enforce within governance, don’t bother.
- Be able to support your definition with supporting details such as the nature of stewardship, required technologies for data quality, or measurement of data governance, but do not bury these details within the definition.