A Fortune 500 company invested over 12 million euros in an AI project for customer churn prediction in 2024. The result? Unusable — because the underlying CRM data was outdated or erroneous in 40% of cases. 80% of all failed AI projects trace back to poor data quality. "Garbage in, garbage out" is not a cliché — it is the most expensive lesson in the AI world.
According to recent studies, data teams spend 60–80% of their time on data cleaning instead of analysis. The four most common problems:
📖 Definition: Data quality refers to the extent to which data meets the requirements placed on it — measured by accuracy, completeness, timeliness, consistency, and relevance.
| Dimension | Core Question | Example | Validation Method |
|---|---|---|---|
| 🎯 Accuracy | Are values correct? | Zip code matches city | Spot checks, validation rules |
| 📋 Completeness | Are all required fields filled? | Email for 90% of contacts | Null-value analysis |
| ⏰ Timeliness | Is data current? | Last update < 6 months | Timestamp evaluation |
| 🔗 Consistency | Are formats uniform? | Date always as YYYY-MM-DD | Format validation |
| 📌 Relevance | Is data suitable for the AI purpose? | Customer data for churn prediction | Business assessment |
💡 Tip: Start with accuracy and completeness — these two dimensions have the greatest impact on AI results. A model like Claude Opus 4.6 delivers poor results even with perfect prompting if the underlying data is flawed.
Before starting an AI project, systematically evaluate your data readiness:
Level 1 — Existence: Do you even have the required data? Level 2 — Accessibility: Can you programmatically access the data? Level 3 — Quality: Does the data meet the 5 dimensions? Level 4 — Volume: Do you have enough data for reliable results? Level 5 — Currency: Is the data regularly updated?
⚠️ Caution: Many organizations skip the assessment and jump straight to the AI tool. That is like building a house on sand. Invest the time — it pays off tenfold.
Immediately actionable measures that noticeably improve your data quality:
🏢 Real-world example: A mid-sized machinery manufacturer conducted a three-month data cleanup before their AI predictive maintenance project. The result: AI prediction accuracy rose from 62% to 91% — solely through better data quality, not a better model.
Rate your most important dataset on a scale of 1–5 for each dimension:
| Score | Meaning | Action Required |
|---|---|---|
| ⭐ 1 | Critical — data largely unusable | Immediate action needed |
| ⭐⭐ 2 | Poor — many gaps and errors | Cleanup before AI deployment |
| ⭐⭐⭐ 3 | Adequate — usable with limitations | Gradual improvement |
| ⭐⭐⭐⭐ 4 | Good — reliable for most AI applications | Build monitoring |
| ⭐⭐⭐⭐⭐ 5 | Excellent — continuously maintained and validated | Maintain |
🔑 Remember: Invest at least as much budget and time in data quality as in AI tools. The best AI is only as good as its data — and doubling data quality often delivers more than switching to the latest model.
🎯 Exercise: Take your most important dataset and rate it on the 5 dimensions on a scale of 1–5. Calculate the average — if it is below 3, you should improve data quality before any AI project.
Next lesson: Structured vs. unstructured data — and how modern AI handles both.
Was bedeutet "Garbage in, garbage out" im Kontext von AI?