Levels of Adoption of Data Governance

Levels of Adoption of Data Governance

StockSnap_CG12GN0QQN_ModernBuilding_LevelsAdoption_BPManagement at your organization might have the assumption that data governance creates a lot of extra work. That is not a totally false assumption. There is effort necessary for data governance and data intelligence. But in terms of total utility and total effort, we think if data governance is done right there is not an increase in the level of effort or very little increase, and the organization gains tremendous value. Data governance makes data management more efficient, efficient, and useful especially in data-driven decision making. Therefore, you get better value out of it too. Before we can look at the effort for data governance, we need to look at the organization’s level of adoption of data governance. In this blog we are going to discuss the four levels of adoption of data governance.

From our experience there are four levels of data governance adoption at an organization:

First Level - Carefree Data Usage
At this level the organization has a carefree data usage. There is no concern or any processes for doing data management or data governance documentation. People are just cranking out reports. That can be a very efficient and fast way to get information out, where you are focusing on productivity. There are times where that becomes very important to do, but you will often get some unanticipated consequences from that. Often at this level there is a lack of trust in the data. And, often, there is a confusion of terms or a collision of definitions. Also, there could be a duplication of reports and silos of information. Which is time and money. As an organization grows, this carefree data usage has a negative impact on the organization, thus the need for data governance.

Second Level – Independent Data Diligence
At this level, many people in the organization have bought into the need for diligence and documentation and research and management over the data deliverables that they are working on or requesting. At this level some of this might be:

  • applying best practices of getting good requirements
  • talking to the experts
  • getting report requests signed off
  • making sure that what is requested has not been done before
  • documenting reports
  • communicating about the data governance efforts

But there are no formal practices or single data-related knowledge base. Much of what the staff is doing may be in little silos of information with a lot of hunting and searching for answers to questions, which is time and money. But at least there is some data governance effort going on.

Third Level – Organizational Data Governance Best Practices with No Data Intelligence Tool
At this level, the organization has started to or adopted some best practices. This might include defined:

  • data steward roles
  • processes for requirements development and data-related requests
  • defined process for data stewardship
  • single knowledge base where data-related content resides and is used for searching

But there is no unified data governance / data intelligence tool in place like the Data Cookbook. Anything being done in terms of data governance workflows, metadata discovery, or general data-related documentation, is using homegrown solutions, spreadsheets, or Word documents.  But there is not a lot of efficient workflows. Homegrown solutions are great as a starting point, but require a lot of effort to manage that on your own. Maybe you are using a ticketing system for workflows and approvals. Maybe you are using some robust documentation management system for keeping track of content. Maybe you have a spreadsheet containing your business glossary. These all work for a while until the management of them gets unwieldy. And there is no way to see how a reference data change affects your data systems or how to track data lineage. Without a tool in place, a deal of effort is required to manage the data governance activities and content. And there are some data governance activities that can not be done without a tool.

Fourth Level – Organizational Data Governance Best Practices with Data Intelligence Tool
At this level, the organization has bought into data governance best practices and has a tool or solution for the data governance efforts, like the Data Cookbook. What is the additive effort of implementing a tool and using this versus not having a tool? One way to think about the difference between levels three and four is if you are going to start a whole workout program. How much does that take to go running on your own outside versus going to a gym or having a treadmill. There's some additional cost and effort in getting those things set up, but there's also a reduction of time as well as being much easier. With a data governance tool, you have a framework in place and a single knowledge base for your data-related content.

Those are the four levels of data governance adoption that have seen.  Take a moment to look at the level your organization's is at. And then determine what needs to be done to get to a higher level – resources, money, tools, etc. to get there.  Remember to start with why you want to increase your data governance efforts (check out this video).  Maybe you need trust in your data, or have data quality issues, or have terminology confusion, or are implementing a new data warehouse.  Whatever the reason, determine the why for the additional data governance activities.  We hope that this blog post helps in your adoption of data governance at your organization. 

Check out these resources regarding adoption and return on investment in this blog postAdditional resources (videos, blog posts, recorded webinars) on data governance and data intelligence can be found at www.datacookbook.com/dg and or in our "Wealth of Resources on Data Governance and Data Intelligence Topics and Components" blog post and is located at: https://blog.idatainc.com/wealth-resources-data-governance-topics.

IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.

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Jim Walery
About the Author

Jim Walery is a marketing professional who has been providing marketing services to technology companies for over 20 years and specifically those in higher education since 2010. Jim assists in getting the word out about the community via a variety of channels. Jim is knowledgeable in social media, blogging, collateral creation and website content. He is Inbound Marketing certified by HubSpot. Jim holds a B.A. from University of California, Irvine and a M.A. from Webster University. Jim can be reached at jwalery[at]idatainc.com.

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