Data engineering for operations and planning
Build a consistent, reliable foundation for planning. Standardise data across systems, automate the updates, and add quality checks so your team works from timely information and shared definitions.
Every analysis starts with two days of cleaning
If someone in your business spends the first part of every month pulling exports, pasting them together, fixing item codes and reconciling two systems that disagree, you are paying for data engineering already. You are just paying for it in that person's time, every month, with a fresh chance of error each cycle.
The symptoms are consistent across every operation we see. Two reports that should match and do not. An item that appears under three codes because three people created it. A supplier name spelled four ways. A unit-of-measure change in 2021 that silently corrupted a year of history. A monthly number everyone quietly adjusts before presenting.
None of this is unusual, and none of it means your team has done anything wrong. It is what happens when systems are added one at a time to solve immediate problems. Data engineering is the work of making that accumulation dependable.
From scattered sources to something you can build on
The foundation is deliberately small: enough to make the next decision reliable, not a platform built in advance of a need.
What we deliver
A pipeline that runs without anyone remembering to run it.
- Automated extracts from your ERP, accounting system, spreadsheets and any other source that matters.
- Standardised item, supplier and location records, with the duplicates resolved and the mapping documented.
- Quality checks that fail loudly — row counts, value ranges, referential checks, freshness — with an alert to a named person.
- A written definitions document, agreed with your team, covering the terms your reports disagree about.
- A scheduled refresh appropriate to the decision: daily for replenishment, monthly for planning.
- A prioritised list of source-data corrections worth making in the ERP itself, shortest-payback first.
How it works
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Map what exists
Which systems hold what, who maintains them, how they are extracted today, and which numbers people already distrust. The distrust is a reliable guide to where the problems are.
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Profile the data honestly
Completeness, duplication, consistency and freshness, measured rather than assumed. This produces a plain summary of what your data can and cannot support today.
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Agree the definitions
A short working session on the terms that matter. Written down, agreed, and then used consistently everywhere.
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Build and automate the pipeline
Extract, standardise, validate, load. Built on standard tooling, version controlled, and documented so another developer could maintain it.
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Add monitoring and hand over
Checks that alert on failure, documentation your team can follow, and a walkthrough with whoever will own it.
What you need to get started
- A list of the systems involved, even an informal one.
- Read access, or a scheduled export, from each.
- Some time with the people who currently do the manual assembly. They know where the problems are better than any profiling tool.
You do not need to tidy anything before we arrive. Seeing the real state is more useful than seeing a cleaned-up version.
Typical outcomes
- The monthly assembly work stops being a person's job.
- Reports that used to disagree now reconcile, because they share definitions.
- Problems surface as alerts rather than as a wrong number in a meeting.
- A foundation the forecasting and inventory work can actually rest on.
Common questions
Is this the same as building a data warehouse?
Usually much smaller. A warehouse is one possible shape, and for a company of this size it is often more than the problem needs. We build the smallest reliable foundation that supports the decisions you want to make, and extend it when a new decision genuinely requires more.
Can you fix our ERP data?
We can clean and standardise extracts from it, and identify the specific records causing trouble — duplicate item codes, inconsistent units of measure, suppliers entered three different ways. Correcting the source records is usually your team's job, but we make the list short, specific and prioritised rather than handing over a spreadsheet of twelve thousand problems.
What are “shared business definitions”?
Written agreement on what common terms mean. Does “on-time delivery” mean against the original promise date or the revised one? Does “revenue” include freight? Most reporting disagreements turn out to be definition disagreements. Writing them down is unglamorous and it resolves an enormous amount.
Do we need this before anything else?
Not always. If your data is good enough for the first decision you want to improve, we start there and build the foundation underneath as you go. We recommend doing this first when the data genuinely cannot support a reliable answer — and we will show you the evidence rather than asserting it.
What happens when a source system changes?
The pipeline has validation checks, so a changed export format or a renamed column raises an alert instead of quietly producing wrong numbers. Silent failure is the most dangerous outcome in a data pipeline, and most of the engineering goes into preventing it.
Find out what your data can already tell you
A free 30-minute data readiness assessment. We look at what you have, tell you what it can support today, and name the first thing worth building. No preparation needed.