“Climate change doesn’t care whether you believe in it or not.” That is a quote I heard on the radio during the drive to my office from a client today. There is data to support the quote, so I tend to believe that climate change is real. However, the data does not seem to deter some people (especially Person 1 in particular) from publicly declaring that “global warming is a hoax.”
Some people live in a world of “alternative facts”. Read my TDAN.com column from January 2017 titled There Are No Facts … Without Data, specifically the section labeled A Fact Does Not Need to be Believable (or Believed) to be a Fact. Climate change is real, but that is not what this blog is about.
There are many organizations that govern and manage their data pretty well. Or should I say— well enough to get by (whatever that means for each organization). But then again there are other organizations that do not govern and manage their data well at all. Some of them know that is true, some do not recognize that as being the case, and then there are those that know they are poor data managers but refuse to act on the knowledge. Which one are you?
You’ll notice that I use the term “govern” along with the term “manage” when I talk and write about data. I keep them together but separate at the same time. I believe that data management is made up of many disciplines of which one can be considered data governance. DAMA International includes Data Governance smack in the middle of their framework. They are very closely related but they are NOT the same thing. Let me explain.
Data Management is an assortment of disciplines that are associated with controlling how data is defined, produced, and used by an organization. Examples of those disciplines include data modeling, data architecture, data quality, metadata management, data interoperability, etc. As you can see, many of these disciplines require an overall data strategy, a plan, commitment, and knowledgeable resources for how they will be addressed. Data Governance is only one of those disciplines and as a practice, often becomes much less technical.
Data Governance focuses on formalizing people’s behavior and accountability for how they define, produce, and use data. I define data governance as the “execution and enforcement of authority over the management of data,” and stewardship as the “formalization of accountability for the management of data.” Both of these definitions focus on people. It could be called “People Governance” according to my friend Len Silverston, popular speaker, author, and practitioner of data best practices.
I started this article by talking about climate change and how there are still people that do not believe it is real. That is more than a real shame. That is simply being ignorant or incapable of understanding the data that demonstrates the problem. I am a believer because I have heard the data— from some pretty reliable sources. I could just as easily become a non-believer if someone would show me the data that the planet was not getting warmer.
OK … Back to data. I wrote an article recently called Look Out for These Six Data Mistakes where I described the top reasons that data governance and data management programs are having difficulties or trouble getting the support they need to be successful. A lot of the problems focus on turning people into believers that improved data management and data governance leads to higher levels of efficiency and effectiveness across the organization. Most of the time it takes real data to convince people that their daily actions are a result of the confidence they have in the quality of the data. And that they have a hands-on responsibility for helping to identify data problems and improve the quality of the data.
Measuring how long it takes to perform simple, or better yet complex, functions may be the best indicator of where people spend time associated with managing the data. In many organizations people do amazing things to get the data the way they need it – to be able to use it. People create reports and pull data sets from primary (and expensive) data resources into spreadsheets, and take many actions on this data.
People do things from deriving their own data (manual dependence on how calculations are defined) to correcting the data (big mistake when changes are not synchronized across data sources). The actions are undocumented and often difficult to repeat or explain. But they certainly have an impact on the results that are used to make decisions, meet compliance requirements, and gain value from the most important data assets. The time people take to understand and trust the data, and how often they change the quality of the data are determining factors in how efficient and effective they can be in their job function. These things can be measured when there is data collected about these things.
Another example of real data to convince people that “data management is real” may focus on the protection of sensitive data. When data handling procedures are dependent on data classification (Hint! All the time!), there are lots of things that can be turned into real data as well. Understanding how people handle sensitive data now, and how their knowledge and behaviors change once they have been showed the rules associated with how data is shared, transmitted, stored, and printed is another way that people’s jobs are affected by a formal data governance program. And they can be measured as well. Occurrences of mishandling data, people’s knowledge of the rules, number of procedures for handling data are formally documented and followed – these are all examples of how to measure data protection – and how to convince people that formal data management and governance have an impact.
It is hard to deny something when there is the data to prove that it is true. Yet that still happens. My suggestion is that you demonstrate improvements by focusing on where people spend their time to get the data the way that they need it to best perform their business function. Data management is real, and people really need it.
