Services
Data platform builds
The foundation everything else stands on. Modern data stack implementations covering ingestion, warehouse, transformation and BI, built on your cloud and built to be owned by your team rather than rented from ours. Milestone-based delivery means you see working infrastructure early, not at the end.
The process we optimise
The process we optimise: how data moves from source systems to the decisions you run on
A data platform exists to carry numbers from where they are created, your finance system, your CRM, your operational tools, to the reports and decisions the business actually acts on. When that path is reliable, every team argues from the same figures and the finance close, the board pack and the daily operational calls all agree. When it is not, people rebuild the same numbers by hand, disagree about which version is right, and slow every decision down. We optimise the whole path, not a single dashboard, so the platform becomes the foundation the rest of your analytics and AI work can stand on.
Before and after
What changes when the system is rebuilt
Before: the reporting nobody trusts
- Reports are stitched together from manual exports pulled out of each system one at a time
- Someone joins those exports together by hand in a spreadsheet, and the logic lives only in their head
- One overloaded analyst is the single point of failure for anything the business wants to know
- The same metric comes out differently in two departments, so meetings start by arguing about whose number is right
After: one platform the business shares
- Data flows automatically from source systems into a warehouse on your own cloud
- Transformations are written down, tested and version-controlled, so the definition of every metric is explicit and shared
- Analysts across teams pull from the same modelled layer rather than rebuilding joins by hand
- One agreed figure per metric, so the conversation moves from reconciling numbers to acting on them
How we think about it
We start from the objective, then work outwards
The same discipline runs through every engagement: understand what the process is for, then design the system to serve it and measure against it.
Start from what the reports and decisions need to say
We begin with the objective of the process, not the technology. We work out which decisions and reports the platform has to serve, who reads them, how often, and what each metric is supposed to mean. That gives us a short, ranked list of the outputs that matter and the definitions they depend on, which anchors every architecture choice that follows.
Map how data actually reaches those reports today
We trace the current path honestly, from source system to final report, including the manual exports, the spreadsheet joins and the undocumented logic that only one person knows. We name where numbers diverge between departments and why. This map is usually uncomfortable to look at, and it is the most useful artefact of the whole engagement, because you cannot fix a flow you have not made visible.
Design the platform around that workflow
We design ingestion, warehouse and transformation around how your data really moves and how your teams really work, not around a reference architecture. Metric definitions become code that is tested and reviewed. Where a manual step was load-bearing, we replace it with something automated and observable, on your cloud, structured so your analysts can extend it rather than depend on us.
Measure the platform against the objective we set
We check the finished platform against the outputs we ranked at the start. Do the reports the business runs on now come from one agreed source? Do the two departments that used to disagree now read the same figure? Does a new question take hours rather than days? We measure against the original objective, because a platform that is elegant but does not settle the numbers has not done its job.
An engagement, step by step
The following is a representative twelve to twenty week arc, not an account of a specific client. It shows the shape a data platform build typically takes and the order the work tends to happen in.
- Weeks 1 to 5
Source audit and objective-setting
We catalogue the source systems, trace how numbers currently reach the key reports, and agree the ranked list of decisions and metrics the platform must serve. We architect the warehouse and ingestion approach on your cloud around those outputs.
- Weeks 5 to 10
Ingestion and warehouse build
We build the pipelines that pull data automatically from each source into the warehouse, replacing the manual exports. You see raw data landing reliably early, which is the first point at which the single overloaded export process stops being a bottleneck.
- Weeks 10 to 15
Transformation and BI layer
We turn the agreed metric definitions into a tested, version-controlled transformation layer, then build the semantic and BI layer on top so analysts query one modelled source. The figures that used to disagree between departments now resolve to one definition.
- Weeks 15 to 20
Handover and platform ownership
We hand the platform to your team with documentation, tests and the patterns they need to extend it. Ownership sits with your people, so the next new source or new report is something they add themselves rather than commission from us.
The engagement is judged on the objective set in week one: whether the reports the business runs on now come from one trusted source that your own team owns and extends.
What you get
- Warehouse and ingestion build on your cloud
- Tested, version-controlled transformation layer
- BI and semantic layer your analysts can extend
- Milestone-based delivery with working software early
How it typically runs
Typical engagement: 12 to 20 weeks, 2 to 3 engineers, milestone-based pricing.
Diagnose and architect
Weeks 1 to 5
Prototype
Weeks 5 to 10
Build to milestones
Weeks 10 to 15
Productionise and hand over
Weeks 15 to 20
Indicative timeline. Every project is scoped individually: book a discovery call and we will provide a detailed proposal within 48 hours.
Where it fits
This practice is delivered through one of the two QuantSpark engines.
Proof
Data platform builds in the field
DataControl Platform: intelligent private equity data management
A mid-market private equity firm
Automating order entry from inbox to ERP
A private-equity-backed European manufacturer of engineered wood products
Building a private equity value-creation data stack with Chronograph
Chronograph