Impact
Your AI strategy is only as good as your data.
Every company can use the same AI models. No one else has your data: your customers, your operations, and what happened every time you made a decision. How well you put it to work decides what AI can do for you.
01 / How you use it
A warehouse describes the past. AI needs data it can act on, and learn from.
Current, not last night’s.
Agents and applications need to act on what is true now and learn from feedback.
Outcomes, not just records.
AI is right most of the time, not all of the time. Capturing what happened after each decision is how you know it works, and how it gets better.
Trusted, not guessed.
AI multiplies whatever it is fed. Governed data compounds into better decisions. Stale or wrong data repeats the same mistake at machine speed.
02 / The bottleneck
AI initiatives stall on high-quality data.
Every use case needs pipelines that bring your data together, keep it current, serve it to applications and agents, and capture what happened. That is data engineering, and it is the capacity most companies are short of. Without data engineering, your AI transformation waits on data.
03 / In production
Same team. 18,000 pipelines.
Medidata, a Dassault Systèmes company, needed a governed data product for every customer. With DataSQRL, the engineering team it already had builds and runs 18,000 customer data pipelines.
- 90%
- TCO reduction
- 3
- Months from Proof of Value to production
- 0
- Additional hires while workload tripled
04 / First step
Put your data to work on one use case.
A scoped Proof of Value on a use case you already have, to get your data flywheel moving and prove the pattern.