Service · IT & Digital
Data Engineering
Three systems, three revenue figures. Which one is right?
Designing data architecture, building pipelines that bring data from source systems together and setting rules for quality and ownership. The result is a documented, reliable data basis for reporting and AI.
Usual
What you often get
A data warehouse that nobody maintains once it's built. Every department pulls its own extracts, and figures get defended in meetings instead of discussed. And when an AI project comes along, the one thing it needs is missing: clean data.
IntoOps
What you get from us
Before any pipeline is built, we agree with the business teams what revenue, an order or an active customer account means. Every data flow checks itself and reports deviations. It's all built for a concrete use, like reporting or AI, never on spec.
Service components
4 components. Each one available on its own.
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Data warehouse or lakehouse design and setup.
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ETL and ELT processes from source systems.
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Rules, checks and cleansing for reliable data.
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Ownership, definitions and access rules for data.
What you'll have at the end. Results you can check.
- A glossary of key figures and terms, agreed with the business teams.
- A data platform with documented sources, refresh schedules and responsibilities.
- Data flows with automated checks that flag errors instead of passing them on.
- Cleansed master data with rules on who creates and changes it.
- An access concept: who may see and use which data.
When you need this. And how you'll know.
- Every department brings different numbers to the same meeting.
- For every report, someone merges exports from several systems by hand.
- An AI project is coming up, and nobody knows whether the data is good enough.
- Master data such as products or customer accounts is duplicated, outdated or contradictory.
Rarely alone. Never bundled.
Data Engineering is usually part of a wider engagement, often alongside these services. You can still ask for Data Engineering on its own.
Shows up in these initiatives.
You don't need a whole initiative to get Data Engineering. These examples show where the service is often needed.
- Project We want to use AI, but properly.
Good questions. Straight answers.
Do we need a data warehouse straight away?
No, not to start with. A small platform with a few clean data flows is often enough. We build what answers your next questions and extend it as new ones arise.
Will you clean up our master data?
Yes. Cleaning up is the easier part, though. To keep it clean, we define who creates, changes and checks data, otherwise the same work soon comes round again.
Does this work with our existing systems?
Yes. Data engineering builds on the systems you have: ERP, CRM, shop, spreadsheets. They're connected through interfaces we build and monitor with system integration.
Can you check whether our data is ready for AI?
Yes, for a specific use case, not in general. We assess volume, quality and access and tell you what's missing. The use case itself we work out with AI solutions.
Proof first. Contract second.
In the free Initiative Check, we listen for 45 minutes and ask the questions that usually only come up after the contract is signed. Then you get a short written assessment: what we see, how we would go about it and roughly what it would take.
No obligation. And if we're not the right fit, we'll tell you.
Or write to us at hello@intoops.com.