Chapter 04
Research dimension: Data Engine
How ready is your data for AI?
Page 7 of 123 min readPublished Published by Teklens
Data is ready for AI when it is accessible, reliable and governed. The DACH analysis shows access is most developed (3.05 of 5), governance sits in the middle (2.73) and quality trails: 2.61 is the second-lowest value of all 19 items, with a median of 2. Most organisations fix data problems after they are reported; systematic monitoring is missing.
Contents · Short answer
Three findings
- 01
Data quality is the second-lowest score in the study
Q11 sits at 2.61 with a median of 2. Level 2 reads "reactive bug fixing: data issues are addressed after they are reported". Only Q18 on adapting structures scores lower.[Probst 2026, Appendix B]
- 02
Access is further along than quality
Q10 data accessibility reaches 3.05 ("documented data sources, access via defined ticket processes"). Q12 data governance sits at 2.73, between "reactive" and "defined".[Probst 2026, Appendix A]
- 03
Intermediate and early stage barely distinguishable
Data Engine is, alongside External Alignment, the only dimension where the intermediate (2.67) and early-stage (2.31) profiles do not differ significantly (Tukey p = .069). The advanced profile sits at 4.11.[Probst 2026, Appendix C]
Evidence
- Q10 Data Accessibility3.05
- Q11 Data Quality2.61
- Q12 Data Governance2.73
Select an item to see its wording, levels and statistics.
Takeaway: Data quality (Q11, 2.61) is the second-lowest score in the study, trailing access (Q10, 3.05) and governance (Q12, 2.73).
- Unit
- Mean on the maturity scale 1 (least developed) to 5 (most advanced)
- Population
- DACH analysis: 64 cleaned self-assessments from Germany and Switzerland, mostly senior decision-makers
- Denominator
- n = 64 organisations
- Source
- Probst 2026, Appendix B · Appendix B – Mean, standard deviation, median, minimum, maximum and item-rest correlation of all 19 items
Data as a table
| Item | Dimension | M | SD | Median | Min | Max | r (item-rest) |
|---|---|---|---|---|---|---|---|
| Q10 Data Accessibility | Data | 3.05 | 1.03 | 3.00 | 1 | 5 | 0.50 |
| Q11 Data Quality | Data | 2.61 | 1.11 | 2.00 | 1 | 5 | 0.66 |
| Q12 Data Governance | Data | 2.73 | 1.03 | 3.00 | 1 | 5 | 0.62 |
Analysis
Adoption runs ahead of data maturity
The Data Engine dimension holds data accessibility, data quality and data governance. Its mean of 2.80 sits mid-field among the six dimensions, but the distribution inside the dimension is uneven: access is regulated, quality is not.[Probst 2026, Table 4]
The European survey shows the same weakness: 41% of the 165 respondents rate their data quality at level 1 or 2. Every AI use case inherits that quality. A model working on incomplete or wrong data delivers wrong results quickly.[AI Monitor 2026, Europe]
Why the intermediate profile does not lead here
The intermediate profile clearly exceeds the early stage on leadership, people and technology. On data, the lead shrinks to 0.36 scale points and is not statistically secured. Organisations in the intermediate profile therefore have pilots, backing and tools, but no better data basis than the early stage. The jump to the advanced profile (4.11) is correspondingly large.[Probst 2026, Table 5]
Teklens interpretation
For your product team
The study describes organisations across sectors. Applying it to software product teams is our interpretation, not an empirical finding about product teams.
Our recommendation for teams whose AI initiatives depend on reliable context.
Name the sources of an Activity
Which tickets, documents and parts of the code feed the task? Who keeps them current? An Activity without named sources runs at level 1 on Q10.
Check quality before automating
Automating bad input only ships the mess faster. Check on a real task whether the sources are right before an agent works from them.
Assign ownership for data upkeep
Level 3 on Q11 reads "regular auditing of central sources". For a product team that can be a weekly look at stale specs and open decisions, with one accountable person.
Scope & limits
- The items measure how respondents assess their data practice, not measured error rates or completeness.
- The 41% share comes from the European survey (level 1 or 2 on Q11), not from the DACH analysis, which publishes means only.
- Extending "data" to product context such as tickets and specs is our interpretation, not the subject of the study.
Questions & answers
Is data quality more important than data access?
The study does not weight the factors. It shows that access is more developed than quality. Having access to unreliable data gains little for AI.
What does level 4 on data quality mean?
"Automated validation in pipelines; anomalies in central sources are detected proactively." The sample median sits two levels below that.
Does data governance not belong under governance & ecosystem?
No. Data governance (Q12) regulates consistency, security and ownership of data and belongs to the technology dimension. AI policy (Q13) regulates responsible AI use and belongs to external alignment.
Sources
The DACH analysis comes from Felix Probst's master's thesis (University of St.Gallen, 2026), which focuses on Germany and Switzerland and was written in collaboration with Studyond and Teklens. Teklens and readyai.one ran the European survey as the pre-study for the Swiss AI Monitor; Teklens, as publisher, provides the interpretation. Full citations and the locator index: Methodology & sources.
- Probst 2026 · Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? Master's thesis, University of St.Gallen, in cooperation with Teklens. Germany and Switzerland, n = 64, maturity scale 1–5, self-assessment, exploratory.Locators used: Table 4 and Figure 4, Table 5 and Figure 7, Appendix A, Appendix B, Appendix C
- AI Monitor 2026, Europe · Teklens & readyai.one (2026). AI Monitor 2026 – European survey on organisational AI readiness (pre-study for the Swiss AI Monitor), raw export of 19 August 2026. Research partners: ETH Zürich and University of St.Gallen. Internal aggregate, published on teklens.ai/ai-monitor.
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