In this blog post we will discuss how data governance and data intelligence is something that is on-going and not a project that is done and put on the shelf somewhere. Data governance content creates value and supports your organization’s goals. Remember that data governance only adds value if there is engagement with the content that supports your goals. The measure of “use” and “adoption” is a balance and exchange between content and engagement.
None of us want to invest our time and energy in creating a binder on a shelf that is never looked at. It is not uncommon for an organization to create a business glossary or some sort of documentation, and it is physically put in a binder and put on a shelf, and no one ever looks at it or uses it. What you want is data intelligence-related content to create value and support your goals. If your goal is data literacy and you have defined some business glossary terms, the only way that those glossary terms are adding value towards your goal of data literacy is if someone in your organization at some point in the future is asking themselves a question and looks at the term to get their answer. For example, we define employee original hire date, someone has a question on the meaning, and they find that content in the knowledge base, and it answers their question. But if no one ever asked that question, there is not a lot of value in documenting that definition. If someone asks that question and does not find it, then that is potentially not creating value. But this is an opportunity for content creation.
Think about the use in terms of points of engagement with data governance content. Value is only added if there is engagement with the content that supports your goal. If people are using this information to advance your goals of better doing self-service reporting or implementing these policies or whatever it is. Otherwise, it is not really adding much value. The types of points of engagement with this content are searching and discovery. For example, let us say someone is trying to do some self-service reporting where they wanted to include the employee's original hire date. They wanted to figure out do we have that? Does that exist in the glossary? If it does, does it tell me where it is and how do we define that and where does it live, in what data system and how do we pull that? Or they are searching for a report. They are going to search through the knowledge base and see if it exists. And if they find it, they are going to be able to discover and get that information back. The second type of point of engagement is that they are looking at something like a report that was delivered to them or is online, and have got a question about that report. They see a link or an attached document that explains the curation and the documentation for that report. And they do not necessarily have to go and search, but they are looking at something, and the supplemental information about this thing is provided with it. Those are your two main ways of engaging with content.
Now, if you do not find what you are searching for, or you find the information you get lacking, or you are looking at something that does not have a reference, then that leads to another point of engagement, which is to request that information. And this is the key point. I cannot find what I want but I realize that this is not the end of the world. I am going to now submit a request to create this definition, or to create this report, or to answer my question, or tell me whether this field is PII. This request will get routed to a data steward for curation to either review, approve, author, write, or create that content. The data steward is getting pulled in to engage in content creation or engage in reviewing content that was created by someone else, based on that request.
The measure of use and adoption should be considered more of a balance and exchange between content and engagement. Are you able to have enough content that people who are doing searching and discovery are finding what they want? And that is a satisfying experience for them. Or if not, if they request that information that you can now add that content in a timely fashion to be of use to them. And a lot of people approach the knowledge base by saying we are not going to share this information in the knowledge base until we have near perfect coverage of content. That is an impossible bar that you have set for yourself. You will never reach that bar. But if you just roll out a minimum amount of content and people go and search and do not find it you must give them a way to request information when they say, "Oh, I didn't find this. Can you get this information for me?". And have them get that information or answer in a timely fashion. In an ideal world, you want to have this balance between what we call a content tipping point, enough content that people feel like there is some chance of finding something, but if not, having a very responsive request process, that they can get an answer back when they need something.
Let's talk a little bit more about these points of engagement. Across all this content, maybe you are searching for a data model in your data catalog, or searching for a term in the glossary, or reporting a data quality issue, or seeing if there is a data quality assessment on an issue that you have seen, or looking at documentation for a report, or trying to understand the lineage of an ETL process, or seeing what is the set of valid values or status codes and what those things mean, or trying to figure out who is the owner of this data? Or maybe looking at another source of data standards or policies. Someone is going to go and do some searching and discovery, and first ask, does it exist? And could I find the information that I am looking for? Or is it documented so can I reference from where I am? Or is it curated, and maybe it might exist, but it does not have a lot of curation or enough information. But can I find enough to answer my question that I originally came into this application to engage with? And if so, great. If not, then I might submit a request. Oh, I didn't find the report I am looking for. Can I get a new report? Or I want to change this report, or I need this report documented and explained, or I need access to the report. I need a definition created, or I think there is a problem with that definition. I want to recommend a change, or I found a data quality problem, and I want to create a new data quality rule, or maybe recommend a new status for reference data, or try to get access to a data system, or any of these sorts of requests. These are the points of engagement. And these requests then should initiate a workflow to get to the right person, to resolve that issue or to create the data governance content as necessary.
We hope that this blog post is a reminder to keep data governance moving forward and improving. Don’t let it be a binder on a shelf not being used. Content should be created with a customer service approach and a just-in-time approach. Allow folks to request new information and get answers to their questions.
Additional data governance resources (blog posts, videos, and recorded webinars) can be found at www.datacookbook.com/dg. Also check out our "Data Governance is Culture - Not a One-Time Project" blog post.
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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