Data Belongs to Everyone

When each user in an organization views data as a personal resource, they can get overly comfortable modifying it until it’s polluted. To avoid this problem, recalibrate your approach so that data is considered a shared resource.
Headshot of author Todd Shepherd
Todd Shepherd
Expert Columnist
October 5, 2020
Updated: October 7, 2020
Headshot of author Todd Shepherd
Todd Shepherd
Expert Columnist
October 5, 2020
Updated: October 7, 2020

Several years ago, I happened upon a Blu-Ray movie sale where I could get three movies packaged together for less than 10 bucks. If the movies are even halfway decent, it’s hard to pass that up. I found a collection of three movies that I very much enjoyed, made my purchase, and fired up my Blu-Ray player as soon as I got home. The first of the three movies I watched was Office Space. If you haven’t seen it, you really should give it a watch. If you’ve ever worked in a corporate office setting, or any office setting for that matter, it’s pretty much required viewing. Even now, hardly a day goes by without me or my coworkers making at least one Office Space reference. If you’ve seen it, then I bet you’re the same.

One scene involves Milton, a squirrelly, oddball character, standing near the table where the manager’s birthday cake is being cut and distributed among the office personnel. Someone passes Milton a piece and he starts to dig in, but a coworker instructs him to pass it down the line to make sure everyone gets a piece. One by one, Milton continues to pass along each piece of cake that comes his way. The final piece is put on a plate and sent down the line, and it stops in the hands of the person immediately in front of him. He looks around at his coworkers, his eyes wide in frustration and disbelief as they enjoy their cake and their conversations. And he doesn’t have either. Every time I see that scene, part of me feels for Milton a little bit. But another part of me thinks that he should’ve spoken up and addressed the issues at hand. Because he didn’t say anything, he kind of got what he deserved. Not to worry, though. Milton gets his revenge in the end. Well, sort of.

We all work with data every day. For some of us, data is our main product. Systems, applications, and processes exist to minimize the havoc that can be unleashed. But no system is flawless, and we can’t underestimate the human brain’s ability to find new ways to circumvent safeguards and cause issues that would make Skynet cry. Fortunately, there are a few things we can do to help avoid a Milton situation in which we’re left darting our eyes around in frustration and muttering about setting the building on fire.

 

Change the View From My Data to Our Data

Many of us have probably approached our work with a limited view of how it contributes to the greater whole in our organizations. We break large projects down into smaller, measurable pieces, which is how it needs to be done. Whoever completes those pieces generally focuses on their deadlines and the particular resources they need to complete their components. There’s absolutely nothing wrong with that approach, either. But in order to meet deadlines, people tend to modify the resources to accommodate the way they work. Depending on what they’re working with, those modifications can cause problems down the road as others need those same resources to do their work. Later users, seeing an already altered resource, make the modifications they prefer, sometimes undoing earlier mods. And on it goes. By the time the resource gets to the end of the line, it’s a mess.

Data is particularly susceptible to these alterations. We tend to view data as a static tool we can use to get our jobs done and meet our deadlines. It’s easy to lose sight of data’s dynamic nature and the downstream effects of any changes we make to it, but the truth is that data is a shared resource across the organization. With this in mind, take a deeper look at your data flows and identify the biggest areas for potential data pollution. Once you know where the trouble spots are, make sure you have updated data validation and security checks to minimize both the opportunities users have to significantly pollute the data and the impacts on it if such an event occurs. This course of action may sound unorthodox, but changing from a “my data” to an “our data” approach isn’t propagating an unusual concept. Instead, you’re actually moving people’s relationship to data to be in line with the real way the organization operates. Undoing upstream data changes is also a potential source of hidden costs, and eliminating those before they happen will put a piece of cake on everyone’s plate.

 

Focus Training on User Empowerment

Like any other change, shifting the way users approach organizational data takes time and effort. Honestly, you aren’t going to get universal buy-in, and you’ll have a certain population of users whose responses range from reluctant to resistant to refusal. Looking at this change from the perspective of the end-user is helpful when you encounter these responses. In this case, the users are faced with the loss of a certain amount of control. Shared data means they can’t make certain types of modifications, which may affect their personal workflows. This forces them to change how they work, and they’re having to make that change in the middle of their daily routine and looming deadlines. It’s helpful to use support calls and any questions that arise as training opportunities here.

Many users need empowerment more than they need instruction. They may know what needs to be done, but they’re reluctant to do it in a new way because they don’t want to mess anything up. Assuage their fears by letting them know that there are data validation and security features in place to help prevent them from making too big a mess. They don’t need to know how the system works, and going into too much detail is likely to make them glaze over and get frustrated. Just keep things focused on how they can get their tasks done, help them identify any changes they need to make in their workflows, and inform them about the guardrails in place that will help minimize downstream data pollution. Some users may need to build their confidence over several such sessions. That’s more work, but taking the time to help them do this will mold them into power users. You’ll also find these people to be solid advocates for you and your department as a whole, not just for the systems you implement and manage.

 

Have a Data Expert Function

Creating these empowerment opportunities implies that there’s someone the users know to contact when they’re having issues or concerns: the data steward. The data steward also serves as the expert on how data should be structured for maximally efficient system use and is the lead on clean-up projects. Not every organization has an official, designated data steward, and there may be numerous reasons for that decision. I’m not necessarily advocating for the creation of a salaried position, although I’d say go for it if you can swing it. You can also go the hybrid route and engage one or more people to take on the role in addition to their current duties. There’s a lot of flexibility in how an organization can fulfill this particular need. The most important thing is that a user support structure should be in place and the data expert function should be served somehow.

Not only does a data steward provide users with a clearly identified point of contact, but it also takes a load off of the technology team. Having a designated data expert function in place streamlines the clean-up of existing data and the establishment of regular procedures to keep data clean for everyone across the organization. Few things gum up the works and cause slowdowns across an organization like polluted data. And once the initial clean-up is completed, it takes ongoing effort to maintain data integrity and quickly respond to correction needs as they arise. Without some sort of data expert function in place, the whole team has to take up the mantle and work it into their daily duties. The resulting disorganization is confusing and frustrating for the users, and it makes effective management nearly impossible.

So do your organization a huge favor and begin finding ways to democratize data ownership. Designate some form of data expert function to implement and maintain your overall data integrity plan. Do your data experts a favor and give them a red Swingline stapler and let them listen to the radio at a reasonable volume. And if you don’t get that reference, do yourself another favor and check out Office Space.

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