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Data Governance Next Practices: The 5 + 2 Model

Originally published December 9, 2010

If you’re a regular reader of this newsletter or one of my blogs, odds are I’ve already ripped the doors off of some of your closely held paradigms about data governance. I’d like to flatter myself and say that this is because I enjoy being provocative and I’m a little sassy. Though both of these statements are factual, indeed empirically tested, the real reason is because I’m with clients all the time and I see what does and doesn’t work. And one thing I’ve learned from overseeing dozens of client engagement is this: There’s no single right way to deliver data governance.

Companies that have succeeded with data governance have deliberately designed their data governance efforts. They’ve assembled a core working group, normally comprised of half a dozen or so people from both business and IT functions, who have taken the time to envision what data governance will look like before deploying it. These core teams then identify the components, putting them into place like a Georges Seurat painting, the small pieces comprising the larger landscape.

Then they make the pitch. That’s right. The core working group is not the data governance council. It’s the design team. These people need to prove to their normally cynical colleagues how data governance will add value and how stakeholders will engage. They need to paint the broad picture so people can stand back and consider it.

Teams that take the time to design data governance can assess different scenarios, ask the right questions, and determine what will work in their organizations. On our projects, we’ve devised what we call the “5 + 2 Model” of data governance. There are five key critical success factors that every data governance effort embraces. Every company should have all 5. We call these Primary Success Factors.

There are also a set of five “optional” factors. These might or might not apply, depending on the company’s incumbent skills and capabilities, its delivery processes, level of business-IT alignment, experience with governance, strategic goals, and overall culture. We find that in that optional group, any two factors usually suffice. We call this group Secondary Success Factors.



The five Primary Success Factors are indispensable to any data governance effort. They are:

Deliberate Design. That’s right. Avoid the “kickoff and cold-cuts” approach to data governance, where someone calls a meeting, orders lunch from Panera, and everyone agrees that data is an asset. Then nothing happens. Instead, the core working group—ideally comprised of data-savvy members from both business and IT—design data governance so that the rest of the organization can buy into a realistic execution plan, not just a sound bite.

Data Management Roles. At Baseline, we distinguish between data governance—the business-driven policy-making and oversight of corporate data—and data management, which is the tactical execution of those policies. We highly recommend solidifying the latter before embarking on the former. Data management roles can include data architecture, metadata management, and data stewardship, to name a few.

Guiding Principles. A company should agree on its common philosophies for data governance before launching its program. Is the data owned by business process owners or application owners, or by someone else? Should data be loaded onto the warehouse if it doesn’t conform to at least one business requirement? There are many possible guiding principles. These will become units of decision making and tie-breaking as your data governance program matures.

Decision Rights. In their book IT Governance: How Top Performers Manage IT Decision Rights for Superior Results, authors Peter Weill and Jeanne Ross maintain that top-performing enterprises assign accountability for decisions that need to be made. Data governance is no different. The authors spend much of their book discussing the importance of appointing decision makers as critical to driving successful governance. Determining the decision-making structures themselves can be complex.

Custom Workflows. In our data governance engagements much of the work goes into designing decision workflows and clarifying handoff-points between individuals or work groups. It’s time well spent. Wait. Let me say that a different way: If you don’t do it, you’ll fail.

And what about those Secondary Success Factors? Hey, wait a minute! Shouldn’t some of those be Primary Success Factors? Aren’t some of them best-practices? Well—and it was only a matter of time before I said this—it depends. The Secondary Success Factors are:

Process Alignment. Aligning data governance, and data ownership in particular, to enterprise business process can be a very effective means of cutting through ownership and scoping debates. It’s particularly effective at companies that are already process-centric, those that have already circumscribed enterprise business processes such as “quote to cash” or “procurement.” However, companies that are not process-aware might find scoping their business processes while introducing data governance too much to bite off at once.

Executive Sponsorship. Sure, it’s nice to have an executive sponsor advocate on behalf of your data governance initiative, but it’s not required. Depending on the company, we’ve seen nascent data governance efforts go perfectly well without an executive trumpeting the message. As long as the effort shows value, it can get baked into prioritization or development processes, ultimately cultivating the support of multiple executives.

Local Regimes. Most companies envision data governance to eventually span the enterprise. Nevertheless, there will be work groups that must—for privacy, regulatory, or other reasons—own and manage their own data. This data is usually tightly controlled and not widely shared. The “regime” that controls it should be beholden to overarching guiding principles (see above), but can devise their own local policies for that data independent of a wider governing body.

Automation. They fix data errors, manage definitions, track data rules and policies, or help data stewards identify data anomalies. They are software tools, and they can help you drive efficiencies and scale your data governance work. Some companies aren’t ready yet. Some won’t be able to sustain or scale data governance without them. Either way, there is a growing list of tools out there to automate data governance work.

A Strategy-Aware Council. It’s rare, but we’ve found that data governance councils comprised of business and IT members who are well-informed about their company’s strategic objectives are those that are willing to invest the most in data governance. This is because the extent to which a company’s data is well-managed is the extent to which that company can achieve its strategic objectives. The higher impact the strategy, the more urgent the data needs.

Companies that have succeeded with data governance usually find themselves with two or maybe three of the secondaries. For instance, the figure below illustrates how our client needed to align data governance and data stewardship to previously designed business processes, each of which was owned by a specific line of business, or regime:



The days of lofty, enterprise-wide, multi-month visioning sessions for data governance are over. Indeed, such approaches to data governance have fostered disaffection. Sometimes well-meaning data governance proponents don’t get a second chance to make their case.

If you’re about to launch your own data governance initiative, convene a working group to envision and design data governance. By the way, YOU are in that working group, and you might turn out to be the leader. You can do it. (If not you, who?) Just keep the 5+2 model in mind as you and the initial team lay the foundation for data governance success.

SOURCE: Data Governance Next Practices: The 5 + 2 Model

  • Jill DychéJill Dyché

    Jill is a partner co-founder of Baseline Consulting, a technology and management consulting firm specializing in data integration and business analytics. Jill is the author of three acclaimed business books, the latest of which is Customer Data Integration: Reaching a Single Version of the Truth, co-authored with Evan Levy. Her blog, Inside the Biz, focuses on the business value of IT.

    Editor's Note: More articles and resources are available in Jill's BeyeNETWORK Expert Channel. Be sure to visit today!

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Posted December 13, 2010 by khashayar jamsahar jamsahar@gmail.com

Hi Jill, What a nice points. you addressed it right. Is there any practical/actionable docs.

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