“On average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error. A full quarter of the scores in our sample are below 30% and half are below 57%. In today’s business world, work and data are inextricably tied to one another. No manager can claim that his area is functioning properly in the face of data quality issues. It is hard to see how businesses can survive, never mind thrive, under such conditions.”- Thomas C. Redmond, Harvard Business Review
Data debt is an increasing problem for organizations and most don’t even know about it. The concept of data debt is derived from the theory of technology debt. Technology debt is the loss an organization incurs when they push a tech decision, activity, or purchase down the road because there’s no will, budget, or resources to deal with it. Data debt measures what an organization borrows when it does not implement basic data governance and management. Data management people have known for years that enormous costs are incurred the longer you delay even the simplest and most basic levels of data management. Data debt now provides an actual number and rationale for that discussion.
Here’s an example: You’ve got Laserfiche. And someone from IT has added a new template field six months ago because of a planned integration with the ERP, they need that metadata for the workflow to grab and auto-fill. However, it’s a field that’s difficult to grab so manual keying is required and no one told the department who batch scans the documents. So, eight months later, IT starts work on the project and realizes, not only will they have to deal with five years worth of backfile conversion, but they also have documents already scanned in missing that field value. Hence, until they decide to move forward. Finance will continue looking up this information every time they need to run this process. Each time someone wants to use that data, they’re spending time (and time means money) in looking it up — and their labor is a cost. Since you know you’re incurring this cost every time, you’ve created a debt. The data debt metric is the anticipated amount of time that will be wasted.
Let’s go through another case, this time we’ll use simple, rounded numbers because although time is money, sometimes it’s best just to do the calculation in dollars. Say your IT budget is $200. You plan to track both actual purchases and data debt. At some point, you pay $20 for a temp, to key in the field data you need to build your workflow. $20 is ten percent of your $200 yearly budget. Now, extrapolate that to a large organization that spends five million on IT. It would cost the organization $500,000 to cover this debt.
One way we help our VIP clients reduce their data debt is through a yearly system check. We drill down into the usage of your system and often make recommendations for metadata, either gathering more or noting fields that aren’t used. Helping you auto-fill the field so you aren’t relying on more staff hours to complete the data extraction.
Whether intentional or unintentional, the mismanagement of data creates debt for an organization. Like all debts, they must be at some point be paid — either over time (and with interest) or in a big investment that pays off the debt.
It’s critical that your data management program addresses data debt. This could include setting a policy for calculating and deciding how much debt is tolerable, determining how to pay off data debt and how best to educate the organization.
So, are you ready to tackle data debt?