Data Quality as a Foundation: Why BI and AI Projects Struggle with 'Dirty' Data
A new BI platform is often enthusiastically received. Until the first user opens a dashboard and immediately sees a duplicate customer, an incorrect address, or a missing invoice. From that moment on, trust in the entire system is damaged, even though the technology is otherwise in good order.
Where 'Dirty Data' Comes From
Data quality issues rarely arise within the dashboard itself. They usually originate in the source systems. Common causes include:
- customer data that is entered twice because sales and customer service work separately;
- connections between systems that have not been checked for years;
- fields that are filled in manually and therefore not used consistently.
As long as no one addresses these causes structurally, every new reporting or AI project carries over the existing problems.
The Hidden Costs of Poor Data Quality
The costs of poor data quality often remain hidden until the consequences become visible: wrong decisions due to incorrect figures and hours spent on manual reporting checks. Perhaps even more costly: an organization that no longer uses the BI platform because it does not trust the figures. In AI, the consequences are even greater: a model that learns from erroneous data reinforces those errors.
Building Quality Instead of Fixing It Later
The most effective approach is to check data as soon as it comes in, not just later in the dashboard. Therefore, set validation rules for the most important fields, automatically detect deviating patterns, and assign a clear owner for each data source. You don't need to set up a heavy governance project for this. Start with the three or four datasets that appear most frequently in dashboards.
This is how you structurally address data quality
1. Start with the most used datasets
Map out which three or four datasets appear most frequently in dashboards and reports. Start quality checks there. This way, you achieve visible results the fastest.
2. Assign an owner for each data source
Make someone responsible for the accuracy and completeness of the data. This way, a quality issue does not linger between departments without anyone addressing it.
3. Build checks into the platform
Automatically check data as soon as it comes in and immediately signal deviations. This prevents errors from only coming to light when someone opens a dashboard.
4. Make improvements visible
Show users which reported data errors have been resolved. Visible follow-up increases trust in the platform and the willingness to work with it.
Data quality requires continuous attention, both technically and organizationally. Those who structurally follow up on errors and make improvements visible increase trust in dashboards and future AI applications. Therefore, Axelio builds standard quality checks into the setup of a data platform and visibly links improvements back to the organization.
Publication November 10, 2026
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