At a recent vendor conference, I found myself talking with a varied group of technology professionals. Two were technology generalists, one was a data engineer, one was responsible for transportation technology at a major university (think autonomous vehicles, traffic sensors, etc.), another was responsible for university student and teacher data (lesson plans, research findings, etc.), and one was responsible for his organization’s IT security.
During the conversation, someone mentioned data governance. Immediately there was a conspicuous and collective sigh around the table.
Our group clearly found the subject intimidating and uncomfortable.
Why does the mere mention of data governance invoke that kind of response?
One reason is probably that the potential scope of a data governance effort is so wide. It could basically involve every possible task associated with data management.
Further, the word “governance” emphasizes the importance of taking those tasks seriously, and getting them right. So when you combine “there’s a lot to do” with “and it’s all important,” fear kindles in the hearts of those responsible.
And rightly so: the consequences of poor data governance are significant. They range from regulatory fines and sanctions for failing to adequately protect data or for noncompliance, to the insidious costs of bad data quality, such as missed business opportunities due to poor decision-making or lost customers due to low service levels.
But there are a lot of “big and important” topics in IT, and they don’t all make a diverse group of seasoned professionals wince. I decided to do some research and dig a little deeper into why data governance seems to be outside our collective comfort zone.
One thing that came up right away is that data governance is defined and described in diverse ways. Moreover, the terms used to describe the activities or responsibilities that comprise data governance aren’t defined or used the same way by everyone. Anytime I tried to define a term, I’d find another term that meant the same thing… sometimes, depending on context. In other words, the definitions tend to morph according to where one looks at them from (our viewpoint).
That variability and inconsistency made just framing this blog post difficult—never mind a program that “…includes the people, processes and technologies needed to manage and protect the company’s data assets…” and impacts an organization at strategic, tactical and operational levels.
Indeed, there’s an axiom in management theory that “You can’t manage what you can’t name.” Further, “You can’t properly manage what you don’t define explicitly.” In other words, how you define a data governance program will significantly impact your ability to manage it successfully.
Given that a key element of data governance is ensuring the consistency of data definitions across an organization, I find it ironic that we don’t have consistent, agreed definition of terms for the components of data governance itself.
Normally when I write about a complex topic, I break it down into a list of subtopics and then decompose each of those—similar to how I would attack a complex software development project or database design endeavor. But all the variability and overlap among terms that I encountered around data governance forced me to change not only my approach to writing this post, but the whole focus of the post.
Instead of working top-down, I had to work bottom-up. Below I listed some subheadings that are parts of data governance, and then I listed all the tasks or responsibilities that relate to all the subheadings. Your mission—if you choose to accept it—is to take a few minutes to decide under which subheading you would place each task.
So here are the subheadings that I started with:
- Data Management (aka Database Management)
- Data Security
- Data Stewardship
- Data Quality
- Master Data Management
- Regulatory Compliance (GDPR, PCI, HIPAA)
Here is my list of many (but by no means all) of the critical tasks that need to be completed in order to ensure that your data is relevant, available, secure, and optimized (i.e., “governed”).
Under which subheading would you put each of these tasks if you were to document your data governance activities?
- Data Encryption
- Data Masking
- Data Access Control
- High Availability
- Disaster Recovery
- Data Lifecycle Management
- Data Version Tracking
- Data Custody Tracking and Control
- Data Provenance Tracking
- Change Tracking and Management
- Data Access Auditing
- Data Update Auditing
- Data Validation
- Define Business Rules for Data
- Meta Data Management and managing consistent data definitions
- Managing Taxonomies and Naming Conventions
Some of the tasks seem to relate to obvious subheading, such as Meta Data Management and Taxonomies and Naming Conventions being grouped under Master Data Management. Or grouping Data Encryption, Data Masking and Data Access Control under Data Security.
But you could group Data Access Control under Data Stewardship as well, along with many other tasks. In fact, Data Stewardship is used somewhat interchangeably with Data Governance… sometimes. And which tasks fit under Compliance? Maybe all of them?
My personal takeaway from all this is that it may be better to look at this particular issue from the bottom up of instead of the top down. When wrapping our minds around data governance, we might want to look at all the relevant lower-level tasks (lower in this hierarchy, not in importance), and think about what is involved in each and what tools can help us implement them.
Don’t get too caught up with definition of terms or with categorizing tasks into subgrouping, as I did for the purposes of discussion. At least when it came to writing this blog post, I found that to be the most intimidating part.