Report
How data scientists escape the 80/20 trap to drive AI value
If 80% of your time goes on data preparation, you have one day a week for the work that actually matters. This whitepaper shows how a knowledge-driven approach changes that ratio.
You became a data scientist to build models, not clean data.
You spend most of your week collecting, cleaning, and reconciling data that wasn’t designed to work together. By the time it’s ready, there’s little space left for modelling or analysis. Every new data source or AI initiative makes it worse.
Access to the right data through the intelligent keyword search framework resulted in higher
productivity and an intuitive experience when exploring the Knowledge Graph.”
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This report is for you if…
For data scientists and analytics leaders who are serious about AI — and starting to see that the bottleneck isn’t the model. It’s the data.
- You spend more time on data prep than on models and want a way out
- You lead a data or analytics team and need AI delivering business value, not just proofs of concept
- You’re responsible for AI governance and need outputs your organisation can audit and explain
What you’ll discover
The practical case for knowledge-driven AI — what it is, why it outperforms pure LLM approaches, and what it means for data science work day to day.
- Why 95% of enterprise AI pilots fail — and how a knowledge-driven approach addresses the root cause
- How semantic knowledge graphs cut data prep time and raise productivity by at least 33%, with a Siemens Energy example
- What explainability, auditability, and FAIR compliance look like in practice for regulated industries
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