In the metadata management discipline, it’s increasingly common to view data as a supply chain that members of the data community jointly use to create, modify, and consume data.
It goes without saying that the members of this diverse community have varying degrees of understanding and insight into the data. In any organization, there are specialized experts charged with guarding the data, interpreting it on a broad scale, and overseeing the relationship between the data and its users. Generally, there are also many others in the community with various roles creating or consuming the data.
That’s all pretty standard stuff. But there’s usually an additional group within community members who can add enormously to metadata management, but rarely if ever attract attention. My tongue-in-cheek term for these individuals is “Data Ninjas.”
Who are the Data Ninjas?
The answer to this question is somewhat complex, because Data Ninjas can be anywhere in the organization, hidden in plain sight. These individuals often don’t even have the words “data” or “analyst” in their titles — yet they understand the data at a deeper level than others whose titles would suggest that they would have the greatest expertise.
In fact, as far as the rest of the data community is concerned, Data Ninjas are basically invisible. Think about the team leader in claims processing whom many people count on to decipher buggy data from the mainframe on certain types of claims. Or consider the programmer on a financial analysis team who has an uncanny understanding of how certain data values in a particular field will trigger delays in quarterly reporting — and who has created an effective workaround.
Yet, as beneficial as Data Ninjas’ insights are, they’re rarely, if ever, captured in a systematic way. As a result, the problems they know about persist unresolved at an organizational level.
Helping your data experts step out from the shadows
By definition, most Data Ninjas are exceedingly hard to find. But it is achievable — if you do your metadata management right. The first thing to know is that there’s no fast and easy way to collect all of the useful metadata (that is, “the data about your data”). Despite what some vendors would lead you to believe, you can’t simply buy their software, execute a program, and have all of your metadata handed to you. There is a type of metadata, that we call conceptual metadata, that resides not in machines, but in the minds of the people who use the data. You need this human perspective if you ever want to get to the truth about your data. the solution for getting conceptual metadata calls for a specialized communication process involving face-to-face, facilitated discussions, occurring over a period of time, among members of your data community.
These discussions must include a diverse set of data stakeholders, and the key is to gather not just the usual suspects — data architects, technologists, etc. — but also business users from throughout the organization. The latter group is where you’ll often find the Data Ninjas, and they’ll emerge naturally in the course of discussions around the data. Your goal is to capture the “tribal knowledge” around your data — and the Data Ninjas often have sole ownership of certain critical insights, at least until someone asks them.
The powerful impact of richer metadata
The approach to metadata acquisition that I recommend delivers multiple benefits. First, as Data Ninjas begin to emerge from the shadows, they can get the recognition they deserve. In many cases, they also become natural candidates for more official roles as data stewards or data custodians. So if only from a standpoint of fairness and efficiency, finding the Data Ninjas and officially acknowledging their expertise is a no-brainer.
But there’s a more fundamental business benefit to identifying and engaging with these invisible data experts. They are often the people in the organization with the most practical insights into certain critical data — that is, the raw materials you need for effective data management. Without their direct input, even the most well-intentioned attempts to improve data quality and reliability, resolve inconsistencies in reporting, and other initiatives can fall short of expectations.
The bottom line is that by arranging for the right kind of facilitated meetings of members of your data community, you can uncover incredibly important insights into your data resources. You’ll be gaining a richer, more valuable understanding of your organization’s data, with a level of granularity never before achieved. As a result, you’ll find opportunities to resolve data discrepancies and connect siloed repositories of similar data that never before talked with each other. On a broader level, you’ll get the insight your organization needs to begin optimizing the quality and accuracy of your data — and ultimately, its security.
Last but not least, you’ll finally be able to begin leveraging what is probably one of your most data community’s most underutilized resources: the practical, nuanced understanding of your data that only your Data Ninjas have. For all these reasons, it’s time for you to find, connect with, and recognize the hidden data experts within your user community.