Companies love bragging about how many dashboards they’ve built. I sat in a meeting where an executive proudly announced, “We have more than 500 dashboards,” as if that number alone proved they were data-driven.
It means nothing, and usually makes me want to ask: How many of those dashboards actually help?
If anything, a massive dashboard count is usually a sign that something is broken. It tells me that the organization is drowning in noise, misalignment and duplicated effort. People don’t trust the data they have, so they keep creating new versions of the same thing.
And it tells me leaders are measuring the wrong thing entirely. The real problem isn’t how much data companies have; it’s how little of it leads to action.
So let me explain why dashboard count is a silly metric, and what I believe companies should be measuring instead.
Dashing Toward Misalignment
No one sets out to build hundreds of dashboards. It happens slowly, almost invisibly. A team wants a custom view. A leader wants a slightly different version of an existing report. A new initiative launches, and someone spins up a dashboard to track it. Old dashboards never get retired because “someone might still use it.”
Before long, you’re staring at a sprawling ecosystem of dashboards, all claiming to be “the source of truth.”
The irony is that the more dashboards a company has, the less truth anyone can find.
I’ve watched organizations spend millions on BI tools only to end up with a library of visualizations that no one uses, no one trusts, and no one can explain. It’s a data landfill.
I once spent an hour with an executive trying to align on the definition of “net sales.” He was comparing two dashboards that didn’t match, because each dashboard was built around a different definition. It wasn’t his fault. Each team had built its own version of the truth.
Dashboards don’t fail because they’re visual. They fail because they surface unresolved data problems without telling anyone what to do about them. When people don’t understand what a metric means, where it came from, or how to act on it, visibility only creates confusion.
When Insight Isn’t in Sight
One of the simplest tests for whether data is actually useful is one that many organizations don’t consider. It’s something I ask every time I look at a report, dashboard or KPI:
What should I do differently today because of this?
After years of sitting in pricing meetings, inventory reviews and rebate calls, I’ve found this question cuts through more confusion than any dashboard ever has.
If the data can’t answer that question, it’s not actionable; it’s just interesting. And “interesting” is where most tools stall out. Interesting data sparks conversation. Actionable data drives behavior. Only one of those creates value for a distributor.
In distribution, this distinction matters more than most leaders realize. They operate in a world where margins are thin, supply chains are volatile and cash flow is king. If the data doesn’t tell someone – a branch manager, a buyer, a pricing analyst, a rebate administrator – what to do, then it’s not helping the business run any better.
Action requires clarity, specificity and relevance to the decisions people make every day.
Here are a few examples:
Margin reports that don’t tell you which orders to fix.
I’ve seen margin dashboards that show trends, averages and heat maps, but they don’t identify the specific orders, customers or SKUs that are leaking profit. A branch manager can’t fix a trend line, but they can fix an order. If the report doesn’t point them to the exact transactions that need attention, it’s not actionable.
An inventory dashboard that doesn’t change buy decisions.
Every distributor has an inventory dashboard. Most of them show turns, aging and stock levels. But unless the dashboard tells the buyer what to buy less of, what to buy more of, and what to stop buying entirely, it won’t change purchasing behavior. It’s just a rearview mirror.
Rebate summaries that don’t accelerate cash recovery.
Rebate summaries often show accrual totals, earned amounts and year-to-date progress. But they rarely highlight discrepancies, missing claims or opportunities to pull cash forward. If the rebate team can’t see which claims to file, which vendors to follow up with, or where data gaps are slowing recovery, it’s just reporting on it.
These examples all share the same flaw: They describe the business, but they don’t direct it.
Every day, distributors are juggling thousands of SKUs, each with its own cost structure, lead time and vendor differences. Purchase orders change constantly; quantities shift, substitutions get made, lines are added or removed, and every one of those changes ripple downstream. Split shipments create multiple touchpoints for what should have been a single transaction. Freight surcharges, tariff fluctuations, and timing mismatches between when product is received, invoiced and sold all add layers of complexity that most industries never have to think about.
The result is simple: Data complexity grows faster than organizational clarity. And the truth is that dashboards often scale confusion around that data.
Ditching the Dashboard Dilemma
Distributors don’t need more dashboards. In reality, if one dashboard is useful – and it’s all you need – just have one. More is not better. What makes dashboards work isn’t volume. It’s whether people trust them, understand them, and know how to take action with them.
Audit what you already have. Many distributors discover that most dashboards are rarely used. That’s usually not because the data is wrong, but because the dashboard doesn’t answer a real, recurring question. Retire anything that doesn’t support a specific action, and rebuild the rest around a small set of known questions, like:
- Which orders are leaking margin?
- What should we buy less of this week?
- Where are inventory imbalances becoming persistent?
Align definitions. If “margin,” “on-hand,” or “earned rebate” mean different things across teams, no dashboard will ever be trusted. Shared definitions don’t just reduce confusion; they eliminate the need for duplicate reports built to “correct” each other.
Make trust visible. For example, add simple status indicators to dashboards: when the data was last loaded, how many error records came through, and whether the report is complete or compromised. When users can immediately tell whether a report is reliable, hesitation drops and action increases.
Constrain interpretation. Actionable dashboards don’t invite open-ended storytelling. They reduce the temptation to extrapolate meaning that isn’t defined in the data. Dashboards don’t create action; people do. The role of data is simply to make the right action obvious, and the wrong action harder to justify.
Designate ownership. Metrics without clear owners don’t get managed. Every meaningful KPI should have a single accountable owner responsible for accuracy and follow-through. Without that, dashboards become discussion aids instead of decision tools.
Even with better dashboards, none of this works if teams don’t know how to interpret what they’re seeing.
Treat data literacy as a habit, not a training event. Reinforce literacy by regularly walking through key metrics in business reviews: explaining what changed, why it changed, and where the data might be imperfect. When teams are expected to interpret the numbers out loud, confidence builds. When discrepancies show up, they become opportunities to fix upstream processes instead of reasons to ignore the data.
What to Measure Instead
If you want to know whether your company is truly data-driven, stop counting dashboards, and start counting decisions. Start looking at whether decisions are getting easier.
Ask whether your pricing team knows which orders need attention. Ask whether buyers can tell what not to buy this week. Ask whether operations and finance can act without first debating whose numbers are right.
Dashboards don’t create clarity on their own. More visibility doesn’t guarantee better outcomes. What matters is whether the data in front of people is specific enough, trusted enough, and close enough to the work to change what they do next.
When dashboards point clearly to action, they earn their keep. When they don’t, they’re just another place to look while decisions wait.
Brandon Lassiter is the chief data officer at ProfitOptics.
This article originally appeared in the July/August issue of Industrial Distribution magazine. Subscribe here and sign up for ID’s Today in Industrial Distribution daily newsletter here.
