Data Quality and AI: Why Your Data Is the Foundation

Garbage in, garbage out - this principle applies to AI even more than to traditional software. An AI model is only as good as the data it is trained on. Data quality is therefore the most important success factor for any AI project.

The 4 Dimensions of Data Quality

• Completeness: Are all relevant fields and records present?
• Consistency: Are same facts recorded the same way? Are formats uniform?
• Timeliness: Is the data up to date? How often is it updated?
• Accuracy: Does the data match reality? Are there input errors?

Typical Data Problems in SMEs

The most common data problems we encounter in Swiss SMEs: data silos across departments, inconsistent naming conventions, missing mandatory fields, outdated records, and manual input errors.

5 Steps to Better Data Quality

1. Data audit: Systematic inventory of all relevant data sources
2. Cleansing: Remove duplicates, standardize formats, fill gaps
3. Governance: Define responsibilities and processes for data quality
4. Automation: Introduce validation rules and automatic quality checks
5. Monitoring: Continuous data quality monitoring via dashboards

Conclusion

Invest in data quality before investing in AI. 30-40% of the AI project budget should be allocated for data preparation. Companies with good data quality achieve 2-3x better results with AI projects than those with poor data foundations.