Dashboards: the trap of unreliable data

The day your dashboards lied to you: why 70% of decisions are biased by poor data quality

Les dashboards have become essential tools for monitoring the performance of a company. Whether it's sales figures, logistics indicators or financial reporting, the Data visualization provides a synthetic view to guide decisions. But what happens when that data is wrong?

Many organizations make strategic decisions based on unreliable data. According to an Experian study, 70% of decisions are influenced by errors, missing or poorly prepared data. Behind careful visuals, dashboards can hide major biases.

Data visualization does not compensate for poor quality data

The power of BI (Business Intelligence) tools has democratized access to data. However, a data visualization is never better than the sources it uses. Invisible errors can contaminate the analysis:

Duplicate or non-deduplicated data
Inconsistencies between databases (ERP, CRM, e-commerce...)
Poorly-standardized categories
Update delays

These problems of data quality can lead to inappropriate decisions. An apparent drop in performance may, in fact, be linked to poor data aggregation or an error in dashboard filters.

The numbers don't lie... but they can deceive

There is a natural tendency to trust the numbers shown on a dashboard. However, the reliability of the data is based on a complex sequence of treatments: collection, transformation, aggregation, visualization. At each stage, errors can creep in and go undetected.

Overconfidence in dashboards can thus lead to biased decision making : reallocation of the budget, adjustment of strategy, stopping a project... when the database used was not correct.

Building a reliable data culture

Setting up a data visualization tool is not enough. It is essential to check the quality of the data in advance used in the analyses. To do this, several practices are recommended:

Set up regular consistency audits
Define data management rules shared between departments
Identify risk areas in data flows
Automate alerts in case of anomalies (missing values, abnormal peaks, inconsistencies between sources)

Developing a culture of reliable data means ensuring that key performance indicators (KPIs) reflect the operational reality of the company.

A dashboard is not a truth, but an interpretation

La data reliability is becoming a strategic issue. Without it, the best reporting tools can only produce flawed visualizations. True performance is not only measured in tools, but in confidence in the data that feeds them.

Before automating or adding new layers of analysis, it is essential to verify that the input data is reliable, consistent, and up to date. Because in a data-driven environment, precision becomes a competitive advantage.