The Hidden Costs of Metrics: Your teams are wasting hours of time for no reason!

In the relentless pursuit of optimization and performance, organizations often find themselves expanding their measurement systems with an ever-increasing array of metrics. As a data person, of course I love me some good metrics! However, while metrics are critical for informed decision-making, there's an often overlooked and substantial hidden cost associated with them. Every time a new metric is added, organizations unknowingly sign up for a series of ongoing costs that can drain resources and consume valuable human capital.

The Unseen, or Unacknowledged?, Burden of Metrics

Adding a new metric isn't just about the initial setup and configuration. Getting the data to flow and the math to work is the easy part! Each one, though, requires an ongoing cycle of validation, verification, troubleshooting, and eventually fixing that can significantly impact an organization’s productivity. In my experience, this hidden cost arises from the time multiple people spend checking and reviewing the details of many, if not all, metrics. It can take years for trust to develop in a measurement system, and the moment that there's one flaw in your IT processes, or an upstream system makes a change that your measurement system didn't know about in advance, that trust clock gets reset. This spawns hundreds of lost productivity hours as all of the metrics are revalidates, and most likely not just one time!

The Cost of Verification

Multiple people will spend hours verifying the accuracy of your metrics. It's just a fact, because most people, especially if the metric is provided from a central place, will either challenge the logic, challenge the underlying source data, or have their own perspective on how the metric should look. Verification is lengthy, too, typically involving:

• Finding the Underlying Details: Delving into the raw data to understand the metric's foundation.
• Downloading the Data: Extracting thousands or more rows of data for analysis, typically into Excel or MS Access.
• Recalculating the Metrics: Building manual spreadsheets to recalculate and verify the metrics.
• Identifying Issues: Troubleshooting discrepancies, which often requires collaboration across departments.
• Tweaking for their Job or Role: even with centralized metrics, different departments will need "one more column" or "a slightly different calculation" that highlights a nuance of their world view

Consider this: if each verification cycle takes a few hours and involves several employees, the cumulative cost over a year can add up to millions of dollars, especially in large organizations with copious amounts of metrics.

If you measure everything...

Trust in metrics is not easily gained. So imagine the multiplicative impact of trying to establish trust across a large organization when you have dozens, or even hundreds, of metrics to track. As H. James Harrington, a pioneer of business process reengineering, once said,

“Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.”

However, if we measure everything, are we measuring anything at all? Humans, even with centralized, fancy tools, simply can't sort out what's important and what's not when they're sifting through a forest of metrics across various web-based tools (or worse, emailed reports!). This is a call to action, then, for companies to step back and not just ask what can be done, but what should be done in order to align their teams on what's really important to move the needle. Having a 'metrics brainstorming' session can be useful, but out of it should come a short list of critical indicators that help management and their teams drive key results.

What we need here is a framework...

Frameworks...guardrails really...are extremely useful to keep these conversations on track.

"If you don’t collect any metrics, you’re flying blind. If you collect and focus on too many, they may be obstructing your field of view."

Scott Graffius in his book "Agile Scrum: Your Quick Start Guide with Step-by-Step Instructions" shares this quote which dovetails nicely with the idea of needing a focused view in order to guide teams to success. In a project I've been engaged on recently, I've shared similar sentiments with regards to having "guiding principles" when considering a metric for our dashboard. The rules are simple:

1. The data absolutely must be from systems and automatically sourced
2. The calculation needs to make sense across the enterprise, horizontally and vertically
3. The metric must be useful for managers to be aware of but important for teams to own
4. The value that the metric derives on performance must outweigh the effort that it'll take to setup and maintain, including future testing, verification cycles, and trust building

The Case for Metric Minimalism

Building off of that last principle, organizations need to critically evaluate the real value of each metric. It’s essential to distinguish between metrics that provide actionable insights and those that add little value but consume significant resources. The focus should be on metrics that:

• Add Value: Provide clear, actionable insights that drive business decisions.
• Can Be Trusted: Are consistently accurate and reliable, minimizing the need for ongoing verification.

Jim Collins, author of "Good to Great," emphasizes,

“If you have more than three priorities, you don’t have any.”

This philosophy can be applied to metrics as well. By prioritizing a few key metrics, organizations can ensure that their measurement systems are not only effective but also efficient.

📌 THE POINT IS: Metrics are essential, but they come with hidden costs that can drain resources. Focus on valuable, trustworthy metrics to optimize both performance and efficiency. It's time to critically evaluate and streamline your measurement systems to avoid the costly burden of frivolity.

Matt Brooks is a seasoned thought leader and practitioner in data and analytics; culture; product development; and transformation. View additional articles by clicking below; like them/follow him to learn more!