Data & Analytics
Data and analytics work is turning the data your business generates into reporting people use to make decisions. The common failure is not a lack of dashboards but a lack of trust in them — numbers that disagree, definitions that differ by department, and reports built around what is easy to measure rather than what matters.
The problem
When nobody trusts the number
Two people bring two different figures for the same metric to the same meeting
Both pulled them from a system, both are defensible, and the next twenty minutes go on establishing whose is right instead of on the decision. Eventually someone is asked to "get to the bottom of it" and the meeting moves on without deciding anything.
Every question queues behind one person
There is an analyst, or a person in finance who is unofficially the analyst, and any question that is not on an existing report goes to them. The queue is long enough that people stop asking, which looks like a reduction in demand for data and is actually the opposite.
The dashboards were built and nobody opens them
A project delivered a suite of reports assembled from whatever the systems could produce. They display a great deal and answer nothing anyone was actually asking, so people export to a spreadsheet and work from that instead — which is where the real reporting now happens.
What a real fix takes
The hardest part of this work is not technical. Getting two departments to accept one definition of "active customer" or "closed deal" is a negotiation about whose numbers change, and it usually goes to whoever will not be embarrassed by the answer — which is why it stalls without someone senior willing to arbitrate. Most reporting disputes are definition disputes wearing technical clothing, and no warehouse resolves one. The related discipline is building backwards from decisions rather than forwards from available data. Starting from what the systems can produce is how you get dashboards that display everything and inform nothing; starting from "what are you trying to decide, and what would change your mind" produces far fewer charts and considerably more use.
Outcomes
What changes
Numbers everyone agrees on
A single definition of each metric, documented and applied consistently, so the meeting is about the decision rather than the discrepancy.
Reporting built around decisions
Each dashboard tied to a decision someone actually makes, rather than displaying everything available.
Self-service that works
Business users answering their own questions instead of queuing for the analyst, which is also what frees the analyst.
Reduced manual reporting
The monthly spreadsheet assembly ritual is more common and more expensive than most leadership teams realise, largely because it is nobody's stated job.
What we build
What we build
Data warehouse or mart design, transformation pipelines with business logic that is tested and documented rather than implicit, metric definitions and a semantic layer, dashboards and reporting, self-service enablement, and automated distribution so reports arrive rather than being fetched.
The parts that decide whether it lasts are unglamorous: tests on the pipelines so a silent upstream change does not quietly corrupt a month of reporting, freshness monitoring so people know whether they are looking at current data, and a semantic layer so a metric is defined once rather than reimplemented in each dashboard by whoever built it.
What you receive
What you receive
The reporting itself, in your BI tool and your accounts, along with the modelled data underneath it — pipelines, transformation logic and tests in your repository rather than as queries embedded in a dashboard where nobody can review them.
The more durable asset is the metric definitions: each one written down, with its filters, date logic and edge-case treatment stated explicitly, applied consistently everywhere it appears. That document is what stops the disagreement recurring, and it is the thing clients most often tell us mattered — because it settles arguments that predate the project by years. Plus enablement, so people can answer their own questions rather than rejoining the queue.
How it works
How it works
Start from the decisions
What are people trying to decide, how often, and what would change their answer. Then work backwards to metrics, then to data.
Definitions
Agreed across the teams who use them, in a room, before anything is built. This step is skipped more often than any other and is the reason reporting projects get relitigated afterwards.
Pipelines and modelling
Ingestion, transformation, tests, documentation.
Reporting
, built against the decisions identified at the start.
Enablement and training
, so self-service is real rather than nominal.
Typical engagement is six to ten weeks, with the first useful reporting usually landing well before the end. The phase that takes longest is rarely the technical work — it is getting agreement on definitions, because it requires people to concede that a number they have been reporting was calculated differently from everyone else's.
Technology
What we work with
Technologies we work with and reach for where they fit. Not an exclusive stack, and the tool matters considerably less than the data underneath it.
Reporting: Power BI, Looker, Metabase or Tableau, chosen by who uses it and what you already run rather than by capability comparison.
Transformation: dbt, so business logic is version-controlled, tested and reviewable rather than living inside a dashboard.
Storage: Snowflake, BigQuery or Databricks where scale genuinely warrants it, and Postgres where it does not — which is more often than vendors suggest.
Orchestration: Airflow, Dagster or your existing scheduler. Extending what you run beats introducing another thing to operate.
Where this applies
Where this applies
Strongest where reporting is manual, where numbers disagree across teams, or where decisions are being made on intuition because the data is not trusted — common in retail and e-commerce, logistics and supply chain and fintech, where operational data is plentiful and reconciliation is a standing cost.
Weakest where the data does not exist yet. Analytics cannot report on what was never captured, and the first fix is instrumentation rather than a warehouse.
Frequently the sensible prerequisite for AI and machine learning work, since clean, consistently modelled data is what makes forecasting and prediction possible at all.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
How we scope and price
Fixed scope, quoted after an assessment. Cost is driven by the number of source systems, the state of the data in them, and the breadth of reporting in scope.
Where source data is poor, cleanup dominates the effort. We scope that explicitly rather than absorbing it silently and running out of time for the reporting itself.
FAQ
Frequently asked questions
Almost always because definitions differ — different date logic, different filters, different treatment of edge cases. Agreeing and documenting definitions is usually the highest-value part of the engagement and the least technical.
It depends on your users and your existing stack. The tool matters far less than the modelled data underneath it, and switching tools is a common way to spend money without addressing why the numbers were wrong.
When the data is not being captured yet — instrument first, report later. When there is no decision waiting on it; reporting built because reporting seems overdue does not get used. When no one senior will arbitrate definition disputes, since the project stalls exactly there. And when a handful of well-built queries would genuinely do, which at low volume they often would.
Beyond a few sources and moderate volume, generally yes — reporting directly from operational databases becomes slow and fragile, and it puts analytical load on systems doing real work. Below that, a well-structured Postgres frequently suffices.
Six to ten weeks typically, with the first useful dashboards usually landing before the end.
Only if it answers questions they are actually asking, which is why we start from decisions rather than from data. Training and enablement are part of the engagement rather than a final-week handover.
Yes, and the foundations overlap substantially. Clean, consistent, well-modelled data is what makes forecasting and predictive work possible, which is why this is frequently the sensible thing to do first.
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