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As with any significant enterprise commitment, you must have a clear and workable plan for your company's data governance initiative. This plan is best described as a roadmap and must incorporate your intentions and strategies and recognize your data maturity and capacity.
This blog will explain what a roadmap is, why you need one, how to build one, identify your milestones and mitigate barriers. You'll learn how a sound data governance strategy will set you up for success and the steps that you need to take to construct a roadmap that will instill a culture of data-driven innovation in your organization.
Data governance plays a vital role in modern business operations. The digital revolution has ushered in the data age, and today the most effective method for growth is through data-driven decision-making.
Yet, data governance can be complicated and expensive if not managed correctly. When you embark on a data governance initiative in your organization, you need a roadmap to guide the process.
Download the Roadmap Template to begin mapping your path to data governance
When presenting your roadmap, you need to ensure that everyone in the organization can see how the drivers directing them to pursue data governance will be addressed. For example, suppose a primary driver for your organization is an effort to discover what your BI assets are. In that case, your roadmap should include a milestone that ensures your BI assets are collected, defined, and curated.
Next, you need to consider risks and barriers that can impede your program. It would help if you preplanned how to avoid or address these issues as they come up and included an escalation process for the leadership team.
Once you understand and prioritize your drivers and have outlined how you will avoid or mitigate risks, you need to establish where you currently are and how you will develop the pillars you need to take your program to the next level.
Companies seeking to develop a roadmap for data governance generally fall into four stages. These include grassroots, uni-dimensional or passive, progressive, and fully functional.
At the grassroots stage, one or two people in an organization will have recognized a need for data governance and will evangelize this need. There is a small budget for some tools that staff is using to the best of their capacity.
At the uni-dimensional stage, commonly known as passive data governance, a tool is deployed by a single team, usually the BI team. All of the brilliance is contained within the individual team, meaning you don't have business members, compliance, quality, infosec, BI developers, security, or architects at the same table, curating terms and managing assets.
Organizations at the progressive data governance stage have adopted a multidimensional approach that includes all company areas. Often beginning with a focus on data literacy, companies continue to progress through data quality, security, and policy. Each committee matures by progressively adding different layers as they gain confidence and master the process.
At the full-functioning stage of data governance, a company has matured in progressive data governance and is working to embed curation and governance requirements daily.
To create a suitable roadmap, you need to establish a starting point. It's ok to have components from more than one of the stages. However, understanding where you are starting from and the various stages in maturing a successful program will help you avoid common pitfalls and clarify your objectives.
Finally, you must consider your organization's capacity. After this, it's time to put your roadmap together. Step one is to establish a Data Governance Office (DGO).
Your Data Governance Office will need to set up a Data Governance Team and Data Governance Committee, pillars, success metrics, communication plan, and training sessions.
To begin, you must establish how you will achieve your Data Governance (DG) Team goals by creating a charter document that details the purpose of the team's responsibilities and priorities. This document will identify a lead and support team and clarify goals and milestones.
Your DG Team is also responsible for establishing and providing continuous support to committees.
Pillars of data governance outline the services that uphold the governance support your DG team is responsible for delivering to others. They include schema comparisons, scheduling crawls, building lineage, data profiling, and more.
Identifying your success metrics early will help you organize them so that you can filter them by individual data governance committee. Next, you can group the metrics to see how your overall program progresses.
Each committee should identify goals related to their success metrics, keeping in mind that they may not all start simultaneously and that these goals may change as they mature.
Your communication plan will determine the areas you want to promote, how the DGO will communicate with governance stakeholders, and how they can expect to communicate with your DG Team. You can also address where people can go if they need or want more information.
Data governance impacts your company's data handling culture. Because of this, you'll need to train users on how to operate tools for specific roles and provide clarification on data governance concepts and their applications. Your DGO should expect to provide a library of different training for various audiences.
The next stage is to focus on your data catalog, data domains, and business glossary, data privacy and access, data quality, and policy management assistance
When you crawl your data environment, you populate your data catalog with metadata about your assets, displaying tables, field names, and ETLs. The goal is to capture as much of your data ecosystem as possible.
A business glossary will help you define your critical data elements. Once defined, you can connect them to your data catalog. This step removes much of the confusion on terminology and provides transparency for metrics, source, and privacy handling.
You should evaluate privacy information handling during the formation of your roadmap. If you plan to expand the capabilities and provide transparency for protected data elements, you can also incorporate these milestones on your roadmap.
Companies manage data quality in many ways, but the key is to evolve from a reactive data quality environment to a proactive one. The goal is not to prioritize quality execution but rather to improve transparency on data quality issues and their resolutions. For example, you should aim for a date when you will include data quality in the conversation when curating terms and assets.
DG Teams will often support how data policies are made available to an organization. Sometimes, policymakers may ask for help from the data governance team to detect policy violators.
Then, the DG Team will help communicate when policy changes have been published and the changes that need to be made within the ecosystem to achieve compliance. The DG Team doesn't make the policies. Instead, they provide the organized space for each area of data governance to share.
Your roadmap should manage each area of data governance in stages along the way. It doesn't always mean implementing full-function data governance from the get-go. Assessing your team, capacity, executive buy-in, data maturity, and more will inform the breadth of your roadmap and enable you to decide where you want to be and how you want to get there.
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