How to Kickstart Your Data Governance Program

Posted in: Business Insights, Technical Track
kickstart data governance

In our previous discussions we reviewed the two primary dimensions to a data governance program. First are the functions necessary to be successful including compliance and risk management, policy, data management, literacy, and architecture. Second is the organizational model for how these capabilities are structured across the enterprise, typically in a mixed model between centralized and federated teams. This structure becomes the foundation for how work is organized, distributed, executed and measured.

 

 

As we kickstart our data governance programs, we must align our primary objectives to our business goals. This alignment ensures our organizational partners see value in our direction, partner on approach and agree on positive outcomes and measures. As a data governance program, our measure of progress internally will be our maturity level today and over time as we invest and become more effective at business impact. By understanding our current maturity and our target maturity we can plan timelines that are realistic and align on key organizational capabilities that support the business’s strategy and key objectives.
Starting a new data governance program can often feel like the easier path because of the lack of established organizational lines, priorities and past commitments. This is only surface deep, if your organization has a gap with existing data governance approaches business teams will often compensate with work arounds, ad-hoc processes, and point-technology solutions. New data governance programs will often have to start by establishing how many teams have these ad-hoc workarounds, the risk and time to rationalize them, and what technology has the potential for wider reuse across the organization. As the person accountable for establishing a new program,  there are several  tasks you must complete to align stakeholders and ensure work is organized and actionable by individual teams.
  • Identify Early Win. All new programs are measured by the speed they can affect positive organizational change. New data governance programs should be built with the goal to identify visible pain points in the organization and swarm to address them quickly. This quickly establishes the program as effective and impactful for stakeholders.
  • Set Priorities. All data governance programs will inevitably have competing priorities spanning technology modernization, process enhancements, investments to lower risk and privacy demands. The established data governance program will clearly establish and communicate their prioritized work, what tradeoffs influenced these priorities and what tradeoffs were made for speed of execution against a number of parallel efforts. These communicated priorities and decision criteria create trust across the organization on the approach of the data governance team and enable others to align their work activities.
  • Roles & Responsibilities. As part of our earlier discussion about determining the optimal organizational model, federated or centralized, we must expand and define the roles and responsibilities for all team members engaged in the program. Roles and responsibilities should include the key activities for each role, how their work is handed off and the rights they have in relation to other teams about awareness of policies, timing for action and inclusion in key decision making events.
  • Cultural Principles. The structure of our data governance program is just as important as the cultural principles we use to make key decisions, communicate our objectives and define our working approach. Principles will vary between organizations based on level of regulatory oversight, countries operated in and other corporate initiatives around growth and employee engagement. Principles will often include lightweight governance, create employee opportunity, treat customer data privacy as a human right or transparency in data usage.
  • Technology Alignment. While the focus of data governance programs must begin with policies, literacy and risk management; technology is a key component for implementation and integration across the enterprise. Establishing a program will involve inventorying existing technology that supports the data governance needs, identifying gaps and including the implementation in prioritization and timeline communications.

Starting a data governance program is not always a fresh and new capability. Oftentimes, it’s a rethink of somewhat established capabilities that are falling behind in speed and capability demanded by the business to balance protecting data and speed of releasing new capabilities. Restarting an established program will have strong headwinds from people’s memory and history of past events.

Other times, data governance programs fail to meet objectives and create institutional memories that will make a restart difficult and require additional effort by the data governance lead in building trust with stakeholders and ensuring their voices are heard in approaches being considered. Restarting a program means addressing past failures, planning for a new approach and establishing a model of active engagement across the organization.

  • Empathy. A history of failed data governance programs can have a long term effect on how an organization views this critical function and influence people’s reactions to new approaches. The entire data governance team must come to all conversations with the necessary empathy to understand those past challenges, internalize them, reflect on the learnings, and ensure that new communications address past pain points while helping the organization move beyond to more successful approaches.
  • Clean Sheet of Paper. Starting with a clean sheet of paper to design your program and priorities can be a powerful tool. This allows the leaders of the data governance program to focus on what is impactful, without the history of past programs that may have failed to deliver the anticipated results. This clean sheet approach allows teams to think about how best to navigate organizational complexities, how to structure teams for maximum effect and communicate in a new language that breaks people of old reactions and habits.
  • Transition Approach. While starting with a clean sheet of paper makes for simpler planning, it doesn’t negate the fact that there’s a history of process, policy, and technology in the organization that must be brought up to our new standards and operating model. Legacy elements present a risk to organizational efficiency the longer they linger. This transition plan must account for the appropriate levels of organizational change, communications, data literacy and technology rationalization to be effective and build trust.
  • Cultural Principles. Just as we have to define culture principles for new data governance programs, a restart of existing programs will be measured on our ability to set direction, ensure alignment and modeling desired behaviors.

Starting your data governance program, either a new one or a restart of an established program is a unique opportunity to establish your cultural principles, your priorities, your roles and responsibilities and address any past trauma the organization may hold on too when executing. Establishing these elements early and clearly communicating enables the organization to feel engaged in key decisions, be aware of why decisions and priorities were established, and what effort they’ll have to assist in the implementation of policies, literacy plans, and technology rationalization.

In our next discussion, we’ll begin to explore how to identify and empower data stewards. We’ll discuss the long-term value of identifying volunteers and empowering them with authority and skills while avoiding conscripting staff into these impactful roles. Make sure to sign up for updates so you don’t miss it.

 

 

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About the Author

VP Analytics
Joey Jablonski is VP of Analytics at Pythian, he leads strategic engagements assisting customers in developing their data strategy, defining and executing on data governance programs and building analytical models to power the modern data-driven organization. Prior to Pythian, Joey was VP of Product at Manifold, where he brought a product mind-set is part of all engagements—allowing for delivery of value quickly in any project, and building over time to drive adoption of new data-centric capabilities in an organization. Joey led engagements across industries including high tech, pharmaceuticals and for the federal government. Before Manifold, Joey held executive leadership positions at Northwestern Mutual, iHeartMedia and Cloud Technology Partners. He brings 20+ years of experience in software engineering, high performance computing, cyber security, data governance and data engineering.

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