Most businesses track metrics, even unintentionally: website traffic, revenue growth, employee productivity, customer satisfaction scores. So the problem isn’t a lack of data. The problem is assuming the numbers we track are the right ones and that we’re using them in a way that matters.
The reality? Many business metrics are vanity metrics. They look good in reports but don’t help you make better decisions. Worse, they can actively push businesses in the wrong direction.
Example 1: when fewer reports mean more risk
A government agency introduces a new system to track security incidents. A year later, leadership is pleased. Reported breaches have dropped by 35%. It looks like security has improved. But in reality:
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Staff fear consequences for reporting incidents, so they stay silent.
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A system change has created unintended barriers. Some breaches can’t be logged.
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The focus on ‘reducing incidents’ has moved attention from mitigation and response to hitting a KPI.
The fix: Instead of tracking only the number of reported breaches, they should ask:
- Has reporting accessibility changed? Are staff encountering technical or procedural barriers?
- What’s the whistleblower culture? Are people afraid to report?
- Are we measuring response effectiveness rather than just raw incident count?
Example 2: when more output isn’t better
A consulting firm introduces a new productivity tracking system. Employees now log how many tasks they complete daily. At first, everything looks fantastic. Task completion rates skyrocket.
But soon, problems emerge:
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Employees prioritise easier tasks to boost their numbers.
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Critical thinking and long-term projects suffer because they take longer to complete.
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Quality declines because speed becomes the primary measure of success.
Here, productivity tracking creates a perverse incentive. Instead of actually improving efficiency, it pushes people to game the system.
The fix: Instead of tracking ‘tasks completed’, the firm should track:
- The business impact of completed work.
- How much time is spent on high-value vs low-value tasks.
- Employee satisfaction and burnout levels.
Example 3: when more sales mean more problems
A fast-growing SaaS company is on a roll. Revenue has doubled in the last 18 months. Investors are excited. The leadership team is celebrating.
But under the surface, cracks are forming:
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Customer churn is rising. People are signing up but not staying.
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Customer support is overwhelmed, which leads to bad reviews.
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Discounting has increased, meaning margins are shrinking.
The company is chasing the wrong metric. Revenue growth looks great in the short term, but long-term business health is in decline.
The fix: Instead of just tracking revenue, they should ask:
- What’s our customer lifetime value (CLV) vs acquisition cost?
- Are we growing in a way that’s sustainable?
- What’s our net promoter score (NPS)? What are customers saying?
How to fix your metrics before they mislead you
So, how do you know if you’re tracking the right data or being fooled by vanity metrics? Here’s a simple framework:
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Apply the ‘so what?’ test. For every key metric, ask: So what? What decision does this number help us make? If the answer is unclear, the metric might be useless.
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Balance quantitative and qualitative data. Numbers tell part of the story. Insights from customers, employees and the market add depth.
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Track leading and lagging indicators. If you’re only tracking revenue, you’re reacting too late. Look at customer retention, employee engagement and operational efficiency: the factors that predict future performance.
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Challenge the numbers regularly. Don’t assume today’s KPIs will always be relevant. As your business evolves, your data strategy should too.
At Veraxis, we help organisations move beyond surface-level metrics to truly understand the real story their data is telling them. Having a strong data strategy is essential. But first you need to question whether the data you have is accurate, meaningful and aligned with your objectives. Otherwise, even the best strategy is built on shaky ground.