This article was originally posted on my LinkedIn profile.

Does your company have a data problem? What I’ve found across industries is that most executives feel that their situation is unique – for whatever reason their company is just “bad” at data. The fact of the matter is that every organization struggles with accurate metrics. There is not a company in existence that has mastered the ability to measure itself accurately and timely. And if a company is doing pretty good, the business environment is changes so quickly that they have to keep their foot on the gas to make sure their metrics change along with it.

A Real World Struggle

In years past I worked with a Fortune-500 company that couldn’t tell you how many customers they had. You heard me right. Surely they had some idea? Or at least a finger in the wind estimate? As I started to interview employees I began understand why this was so difficult for them. This company had grown over time through acquisition and had a dozen or so subsidiaries as part of their enterprise. Each subsidiary had a different definition of what it meant to be a customer. Should a person count as a customer if they were given a free trial but hadn’t paid for a premium service? What about if they put their account on hold? What if they had paid for service but weren’t actually using it? There was no agreement at an executive level on what actually constituted a customer. To complicate things even more, each subsidiary had their own systems and each had their own way of sending data to the mothership, so data was often in different formats that needed reconciliation and was sent at different times, leading to outdated information.

After peeling back layers of the onion, it was clear that the problem was much more complex and ambiguous than at first glance. The most important consequence was how it was affecting the executives and their decision making. Each metric couldn’t be taken at face value and had a litany of footnotes and asterisks to go along with it. They were flying blind and making decisions based on partial or inaccurate information. It wasn’t just about counting customers – many of their metrics, financial and otherwise, were faulty.

Spotting the Symptoms

If this story resonates with you, your company may be facing a similar set of challenges. I’ve seen very similar themes across many clients that are struggling with data. In fact, across clients I have found there to be several “tells” or symptoms that there are deeper underlying issues that should be addressed:

  1. Manual Data Entry – At many companies IT systems are disjointed and misconfigured. In common parlance, we may say they “just don’t talk to each other”. Often highly paid staff (either executives or specialists) spend their time manually entering data a system should already know about. Another thing to look for is heavy Excel use. While it’s a fantastic tool, it does have limits. At a recent client, employees were booking their time into 3 different time keeping systems – not only does that waste employee’s time, but it is also adds unnecessary frustrations.
  2. Different People / Different Answers – If data is siloed and there’s not a common definition for metrics, then asking the same questions in different places will come up with stark differences in answers. A national healthcare company had multiple systems for tracking patient referrals. Because the data was siloed between systems, the sales team and the operations team had different metrics, which not only led to inconsistency in decision making but also increased doubt in the fairness of sales incentives.
  3. Inaccurate, Out-of-Date, Incomplete Metrics – Often times a one-page report that an executive sees has a small army behind the scenes pulling data from various systems, transforming it in Excel, and meticulously laying it out in PowerPoint. This often results in metrics that are out-of-date and hard to take action on, especially when the business is changing rapidly. Given the amount of manual work involved it’s also difficult to reproduce or to dig into metrics when questions are asked. It’s also common to not even have metrics on significant business units. This situation occurred at a for-profit education company during the height of the COVID-19 pandemic when everything suddenly moved online. It was incredibly difficult for the company to measure their performance against metrics given the incredibly manual tasks required to generate the reports. While they fought through it, it took significant time and effort that could have been allocated elsewhere at a crucial time.
  4. The Eager but Unhelpful Vendor – Organizations that stitch together IT systems often rely on vendors for advice on reporting and metrics, but often encounter very eager customer success personnel that have no clue how your business actually functions. They can tell you everything that their product can do, but not what it should do in your particular case. Companies are often left to figure it out for themselves.

Turning Chaos Into Clarity

If you’re company is facing one of these symptoms, then what can you do about it? Here’s the thing, since many industries and organizations have faced similar problems, there are patterns and architectures already established that can help organizations overcome these issues.

  1. Get Everyone on the Same Page – A team should be designated to recommend how the organization should define key types of data, such as customers, sales, opportunities, inventory, etc. The more that each team can agree on a common definition (and then know how their systems differ and game plan how to change that), the more accurate final metrics will be for accurate decision making.
  2. Get All Your Data in One Place – Even the smallest organizations need a handful of systems to operate their business; often organizations have hundreds of applications they use on a daily basis. Getting all the data out of those systems and into a single place is a key enabler for being able to harness the data and put it to its full use. In the IT world, this is usually called a data lake or data warehouse (if you’re really cool, you call it a data lake-house).
  3. Focus on Value – Often companies see all the issues with data and try to solve all the problems at once- which is like a firework – it’s exciting at first but fizzles quickly. Take aim at the most important areas to begin with. Where could the company make meaningful change with a new metric? Is there revenue leakage that could be contained with a new report? Could real-time alerting change daily decisions and significantly increase revenue?

If you’ve experienced any of these issues and would like to talk more, please reach out!