"Garbage in, garbage out" applies more than ever. AI models, dashboards and automations are only as good as the data they use.
Common data quality problems
- Duplicates: the same customer stored several times
- Missing values: empty fields for email, category or price
- Inconsistent formats: dates, phone numbers, units
- Outdated data: addresses and contacts that are no longer valid
- Conflicting definitions: "customer" means different things in sales and finance
- Errors: typos and wrong values
Dimensions of quality
- Completeness – are required fields filled?
- Accuracy – does the data reflect reality?
- Consistency – do systems agree?
- Timeliness – is the data current?
- Uniqueness – are there duplicates?
- Validity – do values follow the rules?
Measuring quality
Automated checks can count missing values, duplicates and rule violations and display them in a data quality dashboard – making progress visible.
Improving quality
- Clean existing data: merge duplicates, standardise formats, fill gaps.
- Prevent new errors: validation in forms, dropdowns instead of free text, required fields.
- Integrate systems so data is entered once and shared.
- Assign owners responsible for each data domain.
- Monitor continuously.
Why it pays off
- More reliable reports and decisions
- Better AI and forecasting results
- Smoother system migrations (for example to SAP S/4HANA)
- Less time wasted on corrections
Start with high-value data
Customer, product and supplier master data usually have the biggest impact.
We include data quality assessment and cleansing in our AI, BI and migration projects.
Planning a project like this? Our AI project assistant asks the right questions for your field and turns your answers into a clear brief – then our team near Munich sends a free, non-binding quote within 24 hours on business days. Start now or read the complete AI & Data guide.