Frame the decision
Agree the question the work must answer, who owns the answer and what would change if the answer were different.
Written decision brief and success criteria
Data Engineering
Data Modelling
The structural work that determines what analysis is possible later.
Problems we are called for
- Models built around a single report
- Unmanageable join complexity
- History that cannot be reconstructed
Scope of work
- Conceptual and logical modelling
- Historisation
- Performance design
- Documentation
How the engagement runs
- Discovery
- Modelling
- Validation
- Handover
What you receive
- Data model
- Migration scripts
- Model documentation
Related
Other work in this practice
Operating position
How we think about data modelling
Most Data Modelling work fails long before delivery: the problem is described as a tool choice rather than a system constraint. We start from the decision the organisation must be able to make, then work backwards through the data, interfaces and failure modes that decision depends on.
Work in Data Modelling is usually inherited rather than designed. Systems accumulate interfaces, exceptions and undocumented behaviour until change becomes expensive. Our first contribution is an honest map of what exists, what it costs and what can safely be removed.
Engagement stance
We would rather narrow a scope than broaden a promise. Engagements are quoted individually after scoping, and we state clearly where the work stops.
Signals
When clients bring us in
A decision is blocked because nobody can state how the current system actually behaves.
Delivery slows every quarter while the codebase and integration surface grow.
Risk, security or compliance reviews keep arriving after design is fixed.
Results are demonstrated in controlled conditions but not reproducible in operation.
If none of these describe data modelling in your organisation, a short scoping call is usually a better use of time than a proposal.
Landscape
What we look at first in data modelling
Before any recommendation, we build a shared picture of the ground. These are the six things we examine, in this order.
01
Decision surface
The handful of choices that actually move cost, risk and speed — and the evidence each one needs before it can be made.
02
System of record
Where the authoritative data lives, who writes to it, and what quietly depends on it that nobody documented.
03
Integration surface
Every interface the work must cross: internal services, vendors, hardware, batch files and the exceptions around them.
04
Operational reality
What the people running the system do on a bad day, and the workarounds that have become load-bearing.
05
Cost drivers
The three or four factors that determine run cost, and whether they scale with usage, data or headcount.
06
Failure behaviour
How the system degrades rather than how it performs when everything is working as intended.
Method
How the engagement runs
A typical data modelling engagement moves through five stages. Each stage ends with something you can read, test or hand to someone else.
Establish the current state
Read the system as it is — code, data, interfaces, operations and the informal knowledge holding it together.
Current-state map with confidence levels
Test the constraint
Probe the assumption most likely to break the plan: throughput, latency, data quality, cost, regulation or ownership.
Measured findings and reproducible method
Design the option set
Present two or three defensible options with consequences, cost drivers and what each forecloses.
Option comparison and recommendation
Hand over deliberately
Transfer the material, the reasoning and the operating responsibility so the work continues without us.
Handover pack and named owners
Questions
The questions this work answers
Most data modelling engagements start because one of these has no confident answer.
01
Is the problem we have been handed the problem we actually need to solve?
02
What would we have to measure to know whether this is working?
03
Which part of this system would hurt most if it failed on a Friday night?
04
What are we paying for that no longer earns its place?
05
Can a new engineer understand this in a week, or only the person who built it?
06
If we stop here, is what we have still usable?
Capability
Where we can take data modelling
Engagements usually begin at one of these layers and move outward only when there is a reason to.
- 01
Assessment
Independent reading of the current system with a stated method, so findings can be challenged on evidence rather than opinion.
- 02
Architecture
Interfaces, data contracts and boundaries designed so the next change is cheaper than the last one.
- 03
Build
Delivery in increments that each carry acceptance evidence and can be stopped without leaving the system worse.
- 04
Operation
Runbooks, alarms, ownership and a supervised period before we step back.
Outputs
What you receive
- A written findings document with method and confidence stated
- A current-state map of systems, data and interfaces
- A prioritised option set with cost and risk consequences
- A decision record the board or sponsor can act on
Engagement boundary
What data modelling work covers
- Engineering, architecture and operating design
- Independent assessment with a stated method
- Delivery with acceptance evidence and handover
What it does not cover
- — Legal, tax or accounting advice
- — Certification, audit sign-off or regulatory approval
- — Claims about outcomes we have not measured
Formats
Ways to work with us
Any of these can carry data modelling work. Pricing is quoted after scoping; there are no published rates.
45 minutes, no charge
Scoping call
We establish the decision you need to make and whether BELTO is the right party for it. If we are not, we say so and point you somewhere useful.
A written summary of what we heard
Two to four weeks
Assessment
A bounded, independent read of the current system with a stated method, ending in findings your team can challenge line by line.
Findings document and option set
Scoped per project
Delivery engagement
Design and build against agreed acceptance criteria, in increments, with evidence attached to each one.
Working system plus handover pack
Fixed term, renewable
Embedded capacity
Senior engineering alongside your team under your direction, with an explicit objective and an agreed end date.
Delivered work and documented practice
Reading
Related thinking and published work
Published positions, research and technical case studies that inform our data modelling work.
Questions
Practical questions
- How quickly can data modelling work start?
- Scoping calls are usually available within a week. Assessment work typically starts two to three weeks after a scope is agreed, depending on access and availability.
- What do you need from us to begin?
- A named owner for the decision, access to the systems and people involved, and agreement on what the engagement must produce. Everything else we can build from there.
- Do you publish rates?
- No. Work is quoted individually after scoping, because the same title can describe a two-week read or a six-month build.
- Can you work alongside our existing suppliers?
- Yes. We define interfaces and responsibilities in writing so accountability stays clear, and we do not take engagements that depend on displacing a supplier to succeed.
- What happens at the end?
- Every engagement closes at a documented decision gate: continue, adjust or stop. You receive the material, the reasoning and named ownership.
Next step
Discuss data modelling
Engagements are scoped and quoted individually; there are no published rates. A first call establishes the decision you need to make, the constraint that governs it and whether BELTO is the right party for the work.
One-pager
Take this away as a PDF
Data Modelling — BELTO one-pager
A single printable page covering what we do here, how engagements run and what to send us to start. Useful for forwarding internally.
PDF · 417 KB