Pete Stiglich has written an article for the Perficient blog regarding the steps to implementing a Data Stewardship subcommittee within a Data Governance program. He writes, “A key initiative to be undertaken prior to instituting Data Stewardship at the enterprise level is to develop a Subject Area Model (SAM) – the 10,000 foot level of detail model. This identifies and defines the key data subject areas found in the enterprise in order to delineate the activities of data stewardship, organizing the lower level components of the Enterprise Data Model, or EDM (of which, the SAM is the highest level component), and for other purposes.  The SAM is used to identify what the subject areas of the enterprise are so that the appropriate Business Data Steward can be identified for the subject areas.”

He goes on, “Some subject areas can be relatively simple and so the work of Data Stewardship might be able to be performed by a Business Data Steward and an assisting Technical Data Steward.  Other subject areas, such as Patient and Provider, can be very complex or require significant oversight (e.g., for regulatory compliance, competition, etc.) and so may necessitate the formation of a Subject Area Data Stewardship Subcommittee, named after the subject area, such as in Figure 1 [above].”

Read more here.

photo credit: Perficient

Visa Europe is building a data governance program with C-suite leadership in order to improve the company’s information management. Leo King reports, ” The company, which processes around 12 billion debit and credit card payments annually and has spent over £400 million on its processing technology, said the function evolved out of a business intelligence project that began three years ago. Paul Fulton, VP of data governance at Visa Europe, told delegates at the Gartner Master Data Management Summit in London today that the company had placed senior leadership on the board of the new function, known as the Enterprise Data Committee.”

King continues, “This was important because of the strategic importance of managing well the vast amounts of information the company processes, he said. ‘Our committee is led by Visa Europe’s chief risk officer, and has a board level mandate,’ Fulton explained. ‘The other members are at VP level.’ The management of data is then checked at a more granular level by specific data stewards. The committee began by setting Visa’s data governance policy and compliance measures, and then appointing the data stewards across functions such as marketing, IT architecture, sales, human resources, and security. Visa Europe operates two data centres in different locations as part of its business continuity and resilience efforts.”

Read more here.

photo credit: Visa

Jay Zaidi has written a new article regarding four pillars that every data governance program must be built upon in order to succeed. He writes, “Governing data across an enterprise in a standard and consistent manner is non-trivial and companies frequently attempt it a few times before they get it right. Some of the reasons for the limited success are a corporate culture that is resistant to change, poor change management practices, lack of sponsorship from the top, lack of education about benefits of the program, scope creep, poor strategy and execution, or budgetary challenges.”

Zaidi continues, “Before embarking on a new program or addressing the deficiencies in an existing one, the following four fundamental concepts related to Data Governance must be understood and agreed upon by key stakeholders: (1) Data Ownership, (2) Accountability, (3) Organization, (4) Transparency.”

He goes on, “Like any enterprise-wide initiative, there are no simple solutions to the Data Governance challenges faced by firms.  However, if governing data is treated as a strategic priority and the Data Governance program is built systematically, sustainable Data Governance is achievable.  While the overall strategy and execution steps will vary for each firm, based on the maturity of its data management practices and Data Governance processes (if initiated), the ‘data challenges’ that it is facing, its appetite for change, the fundamental program-level components, and best practices that must be implemented remain the same.”

Read more here.

 photo credit: Shayne Kaye

Loraine Lawson has written an article regarding how to identify your company’s data pain points and resolve the issues that you discover. Lawson writes, “You’ll often see an admonishment to “focus on the pain points” with data governance and its close cousin, data quality. And that’s good advice, but how do you identify the pain points and how do you know which pains are really worth pursuing. This is apparently a real challenge for companies. One IBM-sponsored study, cited recently in Information Management, found that two-thirds of all companies are implementing or planning to start a data governance project within the next year and a half. But most also said their inability to communicate the value of managing data was a major obstacle for data governance.”

She continues, “Andy Hayler of The Information Difference has a crazy idea: Take the time to actually make a business case for your data governance program. I feel you rolling your eyes out there, but what I really respect about Hayler’s writings is he always takes a very practical, quick approach to building a business case. As Hayler describes it, your business case is the process where you hone in on the smartest starting points for data governance and decide — yay or nay — if that path will pay off in real business value. In other words, it’s the way you hone in on the pain points that really matter.”

Lawson adds, “As it turns out, data governance doesn’t have to be this all-encompassing, massive project. You can actually reap big benefits from focused efforts. In practice, only about 20-30 percent of a company’s business data is actually critical and strategic, according to a recent Information Management article on data governance. Target that data, and you’ll find success.”

Read how to do this here.

 photo credit: Jim_K-Town

According to a new article, “Data governance is the crux of any enterprise data management strategy. Data Governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures. In practical terms, that means putting personnel, policies, procedures, and organizational structures in place to make data accurate, consistent, secure, and available to accomplish business goals.”

It continues, “Effective data governance makes meaningful and correct data available to business and hence it makes business process more efficient by saving money, allowing re-use of data, and supporting enterprise analytics. However, data governance requires more than just a few members of the IT staff with a project plan. It requires participation and commitment of both IT and business management, as well as senior-level executive sponsorship and active consultation with various business communities of interest.”

The writer notes, “In my last company, data governance was planned, managed, and implemented through a three level structure: (1) The Executive Data Governance Council provides strategic direction, ensuring that data governance efforts address all relevant and mission-critical needs of the enterprise. It manages data governance as an integrated program rather than as a set of unconnected projects. (2) The Strategic Data Governance Steering Committee carries out plans and policies to implement guidance from the Executive Data Governance Council. It prioritizes data governance efforts and communicates with stakeholders, users, and other communities of interest. (3) The Tactical Data Governance working group implements plans and policies developed by the EDM Governance team, and analyzes and resolves any tactical problems that arise.”

Read more here.

photo credit: quinn.anya

A recent article offers practical advice for structuring a successful data governance program. The article begins, “A three level model like this can work well at a lot of companies: (1) Data Governance Steering Committee: a cross-functional, executive level group that makes policy decisions, provides funding, resolves escalated issues, and provides strategic direction. (2) The Data Governance Office (DGO) is charged with coordinating data governance (strategic) and stewardship
(tactical) activities. It manages communications from the Steering Committee to all stakeholders. (3) One or more tactical groups (Data Stewardship Teams) in each functional area and geography (if needed), which provide guidance to individuals with data stewardship responsibilities.”

The article continues with a description of each level, starting with the steering committee: “The Data Governance Steering Committee serves a function similar to the U.S. Supreme Court. Issues escalated by the Data Governance Office are resolved by the Steering Committee. The Steering Committee probably won’t directly make much policy (except on an exception basis), except where the issues are serious or the dollar amounts are large. For example, where a new policy may be compliance-related and involve large penalties, the Steering Committee would at least review (and probably sign-off on) the new policy.”

Read more here.

photo credit: HUB

by Angela Guess

Andy Hayler, keynote speaker at last year’s Data Governance Conference, recently wrote an article on basic guidelines that any company can follow in order to create an effective master data strategy: “By its nature, master data stretches across business domains, and so at some point hard questions have to be asked about which is really the definitive product code classification, or materials master, or list of strategic suppliers. IT simply does not have the authority to make business departments change their way of doing things, so getting the business to take back ownership of their data is crucial.”

Seeing this problem Hayler’s organization, The Information Difference, along with a few partners conducted a survey of the data governance practices of 134 companies. The results showed (among other findings enumerated in the article) that, “Only a little over half (55%) of the organisations had a written statement setting out the objectives of their data governance programme… Data governance programmes required a mean of four (median two) dedicated staff, supported by an average of nine (median three) part-time staff… A scary 58% of organisations confessed to not having any form of risk register, with only six per cent having an effective register in place.”

Hayler and his fellow surveyors concluded that the companies with the most successful data governance programs had: “A data governance mission statement, a clear and documented process for resolving disputes about data, policies for controlling access to data, a proper register of business risks, effective logical data models for key business data domains, well documented business processes, regular data quality assessments, a documented business case for data governance, established a link between programme objectives and team or personal
objectives, [and] a comprehensive program of data governance training.”

photo credit: mortsan