Data governance in its 1.0 avatar was all about capturing data and ownership information locally. For specific work, it contexts through tools such as Microsoft Excel or Sharepoint. In the 2.0 wave, data governance systems and practices evolved a little. We could see an improved transparency as both technical and business are being captured in a single view.

However, today’s data reality is very different. For one, the volume of data being generated and captured has exploded. Businesses now want to do more with this data: process it, analyze it, and glean insights. Two, regulations around data (like GDPR) are becoming more stringent and so are compliance requirements. Three, the end users of this data have expanded beyond DBAs, IT teams and data architects to include business users—analysts, cross-functional project teams and CXOs. Their needs and expectations have also evolved rapidly.

“It’s amazing how much data is out there. The question is, how do we put it in a form that’s usable?” 

William Clay Ford, Jr.

The era of Data Governance 3.0

The way we view, process and use big data has changed and so have the principles and focus areas of data governance. Data Governance 3.0 is all about:

  • Automation for smarter, less manual ways of collating data from diverse sources.
  • Connecting different, previously siloed sources hence the flow of data is transparent.
  • Easy-to-use, self-serve interfaces for the end users accessing this data. The end-users do not necessary to have coding expertise.
  • Use of Machine Learning to create recommendation engines and smarter search algorithms.
  • Making data access and validation faster and better.
  • Enabling collaboration across teams through better workflows.
  • Merging of analytics tools with the data management system to make insight generation easier.

From EDM to EIM

Overall, the shift in data governance is towards a smarter, better-planned, proactive approach to information management. Information is essentially data put in context. Thus, while Enterprise Data Management (EDM) is all about designing data structures and capturing data in the best way possible, Enterprise Information Management (EIM) is all about the exchange of data within a business context, leading to the creation and sharing of information.

Designing a Data Governance Program

All business-focused data governance programs have certain universal goals:

  • Enabling better business decision making.
  • Building standard, repeatable, efficient processes.
  • Addressing the needs of all data stakeholders.
  • Enabling collaboration for better effectiveness and reduced cost.
  • Ensuring process transparency.

That said, in most organizations, data governance programs exist to meet specific needs and are usually established as part of larger business initiatives. These could be regulatory requirements/compliance, strategy and business intelligence, data architecture and integration, or data privacy and security. The data governance program must be designed depending on the focus area of the program and its end goals.

Getting Started With Your EIM

Gartner’s Enterprise Information Management Framework offers some interesting insights into the design of business-focused governance programs. At its core, this framework states that an organization’s ‘EIM vision must enable the company’s business vision’. Thus, the first step in designing a data governance program is to define the business’ overall goals which the EIM needs to support.

Next, identify the data, domains, and use cases that will be addressed by the program. At this stage, it is important to understand the current EIM maturity level of the organization and clearly define what the desired state is. This will make the process of creating a roadmap easier. The roadmap must include clear processes, milestones, owners, and timelines.

For any data governance program to succeed, the buy-in of business leaders and all data stakeholders is crucial. So, build a business case for this roadmap that shows how the EIM system will positively impact business processes, outcomes, and/or decision making. Another necessary step to enable data stewardship and good governance is to structure the organization and define clear ownership and supporting roles, reporting structures, and success metrics. Now, the program is ready to be deployed.

Thus, the key to the success of a business-focused data governance program lies in starting out with a clear vision and building it bit by bit until it becomes part of the very fabric of the organization. After all, reaching EIM maturity is a marathon, not a sprint!


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