Data Quality – Why You Can’t Ignore It Any Longer

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by Leah Livingston

Managing your organization’s data can seem like a daunting task, especially after years of piecemeal processes, applications and add-ons have compounded upon one another. Once in this type of situation, getting your “house in order” can seem overwhelming and nearly impossible. However, just like the foundation of your home, ignoring that big crack could lead to many preventable and costly issues down the road. We touched on this topic back in 2015, but this seems like a good time to bring it up again.

While it might not seem like it, good businesses run well by utilizing good data quality. What are we talking about? Good data quality is a core component of your overall data management process, which is the core to efficiently run a business that leads to a great experience for your customers, employees and vendors. It may seem odd, but think about it. How much time and effort are wasted in various areas of the business because of bad data? 

Do any of these situations, or something similar, sound familiar at your organization?

  • During onboarding, a new employee doesn’t receive access to all the systems they need on day one because they’re waiting on another department, or for their manager to manually provide access to x, y and z. Due to this seemingly minor inconvenient administrative process, it takes up to one week to get that employee set-up to even start training.
  • The marketing team doesn’t understand the exact process each sales rep follows to close new sales. Often the reason is because the sales team doesn’t complete their CRM records with helpful information (like the lead source, industry, annual revenue, relevant contacts, etc.), meaning the advertising budget is allocated based on an educated guess, rather than reliable analysis.
  • Rather than spend efforts on patching vulnerabilities in the product, the dev team builds out a process to produce daily uploads of a .csv file for a third-party vendor because the pre-built API from their SDK can’t be used due to poor data quality internally.

Shall we go on? Or are you seeing where this is going?

Organizations often overlook data quality issues in order to “get something done” or move quickly.

Yes, we’ve all heard it before, and we’ll probably hear it again, “We know the data isn’t where it needs to be, but we don’t have time to focus on that right now due to next weeks’ deadline. Can we find a work-around until we have the capacity to handle that project?” Just what you don’t want to hear: the dreaded “work-around”. The problem is, your data quality will never get better until the focus is shifted upstream to its initial input source, and you’re doing yourself a disservice by not focusing on it now. 

Why? Let’s walk through a visualization.

Have you ever played that team building exercise where everyone stands in a bunch and you hold hands with two different people in the group? The goal is to then work together to “undo the knot” until you’re all standing in one big giant circle. The more complicated your organization’s network of applications and processes become, the harder it will be to undo that knot. To add another layer of complication, have you ever played the game only to find that when you undo the circle, your group is in two or three little circles? In the business world, these are silos, which were most likely unintended and probably shouldn’t exist. Wouldn’t you like to have oversight of what’s happening where, and why data is flowing to one place and not another? Of course!

Don’t fret if your organization is in such a situation: you are not alone. It’s often the natural process from the start-up phase. Growth was quick and there wasn’t time to ensure all data loops were closed. At some point, the Law of Diminishing Returns sets in and you can’t ignore the problem any longer, because it’s affecting your bottom line and the security of your infrastructure. Once you identify the problem, you can start working on the solutions. 

Now that we’ve addressed the elephant in the room, let’s talk about how to go from zero to hero in terms of data quality. Tune in for our next post with tips for best practices in data management.

Need help now? We love solving these types of data issues. Schedule some time to meet with one of our consultants to see how we can improve your profit margin through data quality management.

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Idenhaus is an award-winning Identity Management and Cybersecurity services firm based in Atlanta, GA.

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