As promised in my last post, I’m kicking off an ongoing blog series to share my efforts in designing data governance best practices and tools to help Informatica’s customers and partners optimize the value they can derive from their data assets.
To begin, I want to introduce Informatica’s Data Governance Framework. As you can see, the framework is categorized into ten complementary facets. Why facets? When an organization can focus and invest in all the competencies listed, true value – like that of a rare gem – is created.
Okay, the other reason: we’re all bored of the framework graphics with pillars, layers, wheels and bullets. Don’t you agree? Also note: Vision and Business case is actually a single facet with a dotted line through it – so please no snarky comments on the mysterious 11th facet. The dotted line just makes it look a bit cleaner! J
This framework is not tied to any specific data management business objective, use case or technology investments. For example, no matter whether you’re looking to implement an MDM solution to provide a single view of your customer, implement an ILM solution to enable data masking and data privacy policies, or implement a consolidated financial data warehouse to support financial reporting requirements, these competencies must be addressed. Doing so will allow you to scale and support all the above and beyond.
Before walking through our framework, it’s important to recognize that there are many useful frameworks available to you offered by industry analysts, data management organizations, consulting companies and other vendors. No matter whether the framework you use has “Seven Building Blocks” (Gartner), “Seven Elements” (DAMA’s DMBOK), our “Ten Facets” – or something else entirely – when done right they should all share the same message with the same goal. Any framework should provide an outline and checklist for data management evangelists to optimize their chances for success when delivering data management capabilities within and across their organizations.
We have chosen to introduce this framework for data governance because in our experience these ten facets represent the core competency areas organizations should distinctly evaluate and invest.
The Ten Facets Of Data Governance
- Vision and Business Case. Data governance is not about the data. It’s about the business processes, decisions and stakeholder interactions you want to enable. So – why are you asking about data governance in the first place? Combine both a “Big Hairy Audacious Goal” as Jim Collins recommends for your long term vision combined with qualitative Return On Data case mixed with bottom up, quantitative ROI for targeted functions and processes.
- People. In which roles and responsibilities must your organization invest? Who will be your business and IT data stewards? Who’s driving your data governance program? Need a data scientist? What the heck is a data scientist anyway? Partner with your human resources organization to document the required skills, job descriptions, recruiting plans, training programs, career paths, and performance management strategy to ensure your data governance efforts is supported by your best and brightest.
- Tools and Architecture. Evaluate the dependent upstream and downstream systems and applications that must be governed. Understand the data management infrastructure investments that make up your data management reference architecture, and identify what enabling technologies you will need to support your end-to-end data governance and stewardship processes.
- Policies. Business policies and standards are a critical path deliverable for any data governance function. Common policies that must be agreed upon, documented and complied with include data accountability and ownership, organizational roles and responsibilities, data capture & validation standards, information security and data privacy guidelines, data access and usage, data retention, data masking and archiving policies.
- Organizational Alignment. Who will be the executive sponsor for your organization’s data governance efforts? Will there be an executive steering committee? Who are the business data owners? What are the escalation paths for policy and data conflicts? Are your data steward’s full-time or part-time roles? Do the stewards hold solid-line or dotted-line reporting relationships to the executive sponsors? Who’s collaborating with whom? This is where a DACI or RACI roles and responsibility matrix comes in extremely handy. (DACI defines roles of Driver, Approver, Contributor and Informed. RACI suggests similar roles of Responsible, Accountable, Consulted, and Informed).
- Measurement and monitoring. Data governance must be measured at three distinct levels. First at the program level to identify and highlight the qualitative level of organizational influence and impact the DG efforts deliver. Next is the quantitative business value measurement that links data management efforts to real business value such as revenue growth, cost savings, risk reduction, efficiency improvements, customer satisfaction, etc. Finally operational data monitoring is required for the stewards to evaluate how the data is behaving against expected policy and validation baselines.
- Change Management. No matter how compelling your vision and business case, making data a true corporate asset is a major culture shift for most organizations. To accomplish this move from current to a desired future state, significant behavioral change will likely be required across your workforce – and maybe even your partner ecosystems. Support for these organizational, business process, and policy changes will require significant training, communication, and education with a “carrot and stick” performance management program to incentivize good data practices while discouraging damaging behaviors from the past.
- Dependent Processes. You can’t govern critical enterprise data until you first understand the life cycle of the data. These are the upstream business processes that create, update, transform, enrich, purchase or import data, as well as downstream operational and analytical processes that consume and derive insight and value from data. These consuming processes are usually top of mind, as they often make up the crux of your business case. But to deliver trusted, secure data, the biggest change often must come from the upstream processes that may be responsible for much of the “garbage in/garbage out” situation your data governance effort is built to resolve.
- Program Management. A multi-phase, multi-year plan for starting small and growing into cross-enterprise, self-sustaining holistic data governance doesn’t manage itself. Whether through an official program management office (PMO) or a team of program drivers, your data governance efforts needs skilled project/program management professionals to coordinate the complex interactions, communications, facilitations, education, training and measurement strategy. Effective program management can ensure adoption, visibility, and momentum for future improvements.
- Defined Processes. These are the stewardship processes that make up your data governance function. These include the processes that cleanse, repair, mask, secure, reconcile, escalate, and approve data discrepancies, policies and standards. We have defined over twenty distinct processes segmented into core process stages of discovery, definition, application (of rules/policies) and Measure & Monitor. A future post will drill down into each of these process stages in much more depth – stay tuned!
This post introduced each facet. I will write a separate post for each facet over the coming weeks to dive deeper into each competency. Until then, I’d love to hear your feedback. Is the framework complete, or is there anything critical path that we’ve missed?