More data never improves data-driven decision-making.
Dashboards often overwhelm. Their creators believe they are adding value. They aren’t. They are hiding the evidence you need in plain sight.
That’s your challenge when it comes to making data-driven decisions: you must be clear about the metrics that matter.
I faced this challenge while managing a network of 200 service providers. We had a huge amount of data. At times, it felt like we were drowning.
When I joined, the software used to manage work allocation had been running for nearly a decade. Adding business intelligence software a few years later made it even easier to collect and analyse data points.
Along with this flood of information, we had a more pressing issue: poor performance.
This wasn’t just visible in the metrics. Our support team was under strain, and clients were giving us negative feedback. The threat of losing them—and missing our revenue goals—kept me awake at night.
One awkward drive back from a client meeting revealed my boss’s concern. “We have to find out why our service levels are so bad. If it costs us clients, it will cost us our jobs!”
Our metrics showed where performance was poor, but not why.
Past unit leaders had increased internal support, which cut into profits, but performance still hadn’t improved.
With 250,000 repairs a year, the law of large numbers told us the answer was in the data.
But where exactly?
So, what did I do next?
I decided to look at what really fed the system. Every service level we monitored focused on outcomes, not inputs.
What were the inputs that drove the system? We didn’t need more data—we needed to subtract the noise.
But first, I had to dig into the basics of our operation.
Some digging revealed an algorithm that allocated work based on skillset, area, and capacity. The capacity numbers were easily manipulated. Typically used when a service provider went on holiday or had to be temporarily stopped.
Often, the capacity number was set to unlimited, so any new job was immediately allocated. Otherwise, jobs went unassigned—which was seen as a big problem.
This system actually created the wrong incentives, which made things worse.
My intuition was like a car alarm, impossible to ignore for those willing to listen.
So, we looked at how the capacity numbers were calculated.
The answer: the number of engineers multiplied by eight jobs a day, multiplied by the days in the week.
This was the root cause. We were allocating work based on total capacity, not the work available after accounting for other jobs.
To test this idea, we took our worst-performing service provider and allocated work based only on his available capacity.
His service levels improved on every metric—and his completed jobs stayed the same, so his income didn’t change.
By focusing on fewer, more meaningful metrics, we found the real input: available capacity. We quickly built a model that accounted for private work and jobs for other companies.
Within a day, we had a baseline of capacity versus work in progress.
The difference was remarkable.
We could now see things like:
Above all, we found the secret to growing this business: more service providers.
But I discovered an even greater truth.
More can only follow less.
It sounds obvious, but it’s something many forget in the rush to scale for revenue growth. The leap from zero to many often skips the fundamentals. But here’s the catch—you can only add more after you’ve subtracted the unnecessary.
That’s what happened with our repair network.
We didn’t start with a few jobs; we scaled to 100,000 jobs in the first year. Impressive, but along the way, the core principles became unclear.
Many businesses find themselves in this position.
By focusing on available capacity instead of total capacity, we transformed our service levels almost overnight. The real insight?
More data isn’t always the answer.
Sometimes, clarity comes from subtraction, not addition.
Remember, dashboards often overwhelm. But by zeroing in on the right metric, you can unlock real value.
Next time you’re drowning in data, ask yourself: What can you subtract to find clarity?
