.png)
Every business wants to become “data-driven.” Leaders are investing in ever more sophisticated dashboards, exploring the promises of artificial intelligence, and recruiting experts to make the most of their data. But an invisible obstacle persists, and it is not technological: it is human, organizational, daily. Data used on a day-to-day basis is often incomplete, inconsistent, or just plain wrong. Most of the time, no one immediately recognizes the impact of these imperfections. However, the bill is very real: erroneous forecasts, poorly sized stocks, ineffective marketing campaigns. Gartner has encrypted it: on average, $12.9 million per year per business. This cost never appears on the balance sheets, but it silently eats away at performance.
Reducing poor data quality to an IT subject would be a mistake. It directly influences the profitability of activities. An input error in a CRM can distort an entire campaign. A duplicate in a customer base can trigger two invoices instead of one and generate frustration. Outdated data used in a dashboard can steer a strategic decision in the wrong direction. Each isolated error seems benign. Together, they constitute an enormous financial burden that hampers growth.
These costs come in multiple forms. In the operational field, entire teams waste hours verifying and correcting files instead of creating value. On a commercial level, poorly calibrated segmentation leads to wasting marketing budgets. On the financial level, an inconsistency in an ERP distorts the calculation of margins and delays the collection of invoices. And above all, at the managerial level, trust is eroding. When decision-makers doubt their dashboards, they slow down their decisions or go back to instinct, depriving the company of the strategic contribution of data.
Take the supply chain, a sector where data accuracy is vital. A false forecast of just 15% can be enough to cause excess inventory, tie up capital, and increase logistics costs. In retail, poor expectations of demand can lead to critical disruptions in the midst of a commercial period. The same goes for marketing: targeting the wrong customers at the wrong time is not only ineffective, it's destructive. This degrades the brand image and loses growth opportunities.
The good news is that this problem is not inevitable. Data quality can become a strategic driver if it is treated as a shared responsibility. Every department should feel involved, because every interaction with data counts. Setting up simple indicators — completeness rate, consistency, freshness — makes it possible to monitor its reliability with the same seriousness as financial KPIs. Automation complements the effort: today, AI detects anomalies, eliminates duplicates, and harmonizes databases faster and better than any manual intervention.
Investing in data quality is not a luxury or a secondary project. It is a way of securing decisions, of avoiding hidden losses and of freeing up teams' time for missions with higher added value. Data quality is not a cost center, but a direct source of ROI.
.png){kind=logo}
.png){kind=logo}
