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Data & Analytics

Numbers everyone trusts, in one place, updated overnight.

A warehouse your data actually lands in, modelled metrics with definitions people agree on, and dashboards that answer the question instead of presenting forty charts.

Book a discovery callBuild your plan
  • Dubai head office, GCC-wide delivery
  • Arabic and English throughout
  • Written scope and fixed timeline before you commit

You walk away with

  • Warehouse build
  • ELT pipelines
  • Semantic layer
  • Executive dashboards

Typical timeline

6–14 weeks

Typical engagements start around

AED 22,000 per project

What good looks like

definition per metric, in version control
1definition per metric, in version control
freshness target for overnight reporting
1 dayfreshness target for overnight reporting
of dashboard figures traceable to source
100%of dashboard figures traceable to source

The problem this solves

Three departments bring three different revenue figures to the same meeting and the meeting becomes about whose number is right. Reporting is a person exporting CSVs for two days every month. Nobody trusts the dashboard, so everyone keeps their own spreadsheet, which guarantees the numbers stay different.

Where this usually hurts

  • Three teams, three revenue figures

    The meeting becomes an argument about whose number is right, because each report defines revenue differently against raw tables.

  • Reporting is a person exporting CSVs

    Two days a month of manual work, which means the numbers are always stale and the person doing it cannot do anything else.

  • Nobody trusts the dashboard

    So everyone keeps a private spreadsheet, which guarantees the figures stay different and the dashboard stays untrusted.

  • Forty charts, no decisions

    The dashboard is comprehensive, nobody opens it, and the questions people actually ask are still answered by asking someone.

We build the layer that makes a number mean one thing: sources landed into a warehouse on a schedule, transformations in version control with tests, a semantic layer where every metric has exactly one definition, and dashboards on top that are deliberately small. The hard part is not the pipeline. It is getting finance, sales and operations to agree on what active customer means.

One definition per metric

Most disagreement about data is not a data problem, it is a definitions problem. Before modelling anything we run a definitions workshop and write the results down: what counts as revenue, when a customer becomes churned, whether refunds net off in the period they occurred or the period of sale. Those decisions live in the semantic layer, so every dashboard and export inherits the same answer.

  • Every metric defined once, in version control, with an owner
  • Transformations tested — freshness, uniqueness, referential integrity — on every run
  • Lineage from dashboard back to source, so any number can be traced
  • Changes to a definition reviewed like code, because that is what they are

Dashboards that answer a question

A dashboard with forty charts is an admission that nobody decided what it was for. We build one per decision, starting from the question its audience actually asks, and we delete charts that nobody has opened in a quarter. Fewer, sharper views get used; comprehensive ones get bookmarked and forgotten.

If a chart cannot change what someone does this week, it belongs in an appendix rather than on the front page.
— How we review every dashboard before handover

Residency and access, decided up front

Analytics stacks quietly move data across borders — a hosted warehouse in one region, a BI tool in another, extracts cached in a third. For regulated Gulf clients we build in-region and keep row-level access tied to your existing identity provider, so a regional manager's dashboard shows their region because of their login rather than because of a filter they were asked to remember.

Forecasting, only once the basics hold

Predictive work on top of untrustworthy data produces confident nonsense. Once the warehouse is stable and the definitions hold, demand forecasting, churn scoring and cohort analysis become genuinely useful — and we will tell you when a simple trend line is doing the same job as a model for a fraction of the cost to maintain.

1 day

typical freshness target for overnight batch reporting

What is included

  • Source audit

    What data exists, where it lives, how reliable it is, and which sources are worth landing first.

  • Warehouse build

    Warehouse provisioned in your chosen region, with environments, access control and cost controls.

  • ELT pipelines

    Scheduled ingestion with monitoring, retries and alerting when a source stops arriving.

  • Semantic layer

    Metrics defined once in version control, tested on every run, with lineage back to source.

  • Dashboards

    One per decision, built with the people who will use them, in Arabic and English where needed.

  • Enablement

    Training for analysts to extend the models themselves, plus documentation of every definition.

What we build with

  • Warehouse

    • BigQuery
    • Snowflake
    • PostgreSQL
    • Databricks

  • Pipelines & modelling

    • dbt
    • Airbyte
    • Fivetran
    • Airflow

  • Visualisation

    • Power BI
    • Metabase
    • Looker Studio

How engagements are sized

Three sizes, so the scope matches the problem rather than the budget matching a template. Every tier is a starting point — the number moves with what we find in discovery, and you see the revised figure before anything is signed.

  • Essential

    One clear problem, scoped tightly and shipped.

    From

    AED 22,000

    per project

    A warehouse and a handful of trustworthy dashboards from two or three sources.

    • Up to 3 sources
    • Core metric layer
    • 3 dashboards
    Get a firm number

  • Growth

    The full engagement, with measurement and iteration built in.

    From

    AED 65,000

    per project

    The full stack across your systems, with a semantic layer and enablement.

    • Unlimited sources
    • Full semantic layer
    • Analyst training
    Get a firm number

  • Enterprise

    Multi-entity, regulated, or integrated across several systems.

    From

    AED 165,000

    per project

    In-region residency, row-level security by identity, or predictive modelling.

    • In-region deployment
    • Row-level security via SSO
    • Forecasting and cohort models
    Get a firm number

Prices are in AED excluding 5% VAT. Other currencies on the pricing page.

How delivery actually runs

The phases below are what a typical engagement moves through, with the durations we plan against. You always know which phase you are in and what leaves it.

See the full process
  1. 1

    Definitions workshop

    1 week

    Get finance, sales and operations in one room and force a decision on what revenue, active customer and churn actually mean. Written down, with owners.

  2. 2

    Warehouse and pipelines

    2–4 weeks

    Warehouse provisioned in the region you need, sources landed on a schedule, with monitoring and alerting when a feed stops arriving.

  3. 3

    Model and test

    2–5 weeks

    Transformations in version control with tests for freshness, uniqueness and referential integrity, and lineage from dashboard back to source.

  4. 4

    Dashboards and enablement

    2–3 weeks

    One dashboard per decision, built with the people who will use it, then training so your analysts extend the models themselves.

  • Definitions workshop · You get

    • Metric definitions
    • Named metric owners
    • Source system audit

  • Warehouse and pipelines · You get

    • Warehouse with access control
    • Scheduled ELT pipelines
    • Freshness monitoring

  • Model and test · You get

    • Tested data models
    • Semantic layer
    • Lineage documentation

  • Dashboards and enablement · You get

    • Decision dashboards
    • Analyst training
    • Documented definitions

Built for the UAE

What working with a Dubai partner actually changes

Most of what follows is invisible until it goes wrong — a supplier who cannot keep data in-country, an invoice the FTA rejects, a support rota that is asleep for half of your working day. These are the questions we answer before they become findings.

Analytics stacks move data across borders quietly — a warehouse hosted in one region, a BI tool in another, extracts cached in a third. For regulated Gulf clients we build in-region and tie row-level access to your existing identity provider, so a regional manager sees their region because of their login, not because of a filter they were asked to remember.

  • Data residency, decided up front

    AWS me-central-1, Azure UAE North and OCI Dubai are all in scope. We map the whole data path — backups, logs, metrics and support access — rather than the primary database alone, and put the result in writing.

  • PDPL, and the free zones

    Federal Decree-Law No. 45 of 2021 governs most of the mainland; DIFC and ADGM entities fall under their own data protection laws instead. We establish which applies to your entity before we design anything that touches personal data.

  • VAT and e-invoicing that pass

    Five per cent VAT, FTA-compliant tax invoices, the audit file the authority can request, and readiness for e-invoicing as the mandate phases in. Configured in the build, not patched after the first filing is rejected.

  • Your hours, your languages

    Delivery runs on Gulf hours with real overlap with your team, in Arabic and English. Customer-facing interfaces, documents and training are produced in both where you need them, with RTL treated as a design requirement rather than a translation step.

Sectors we do this in most

Not the only sectors we work in — the ones where we have shipped this service enough times to know the regulatory edges and the usual traps.

  • B2B SaaS

    Marketing sites and product UX that feed qualified pipeline.

  • E-commerce & Retail

    Fast storefronts and checkouts engineered to lift AOV.

  • Fintech

    Onboarding, KYC and dashboards that build trust and convert.

In-house, generalist agency, or us

An honest comparison, including where the other two options are the better call.

Hiring in-houseWorking with us
Time to first outputThree to five months to source, notice-period and onboard, in a market where senior specialists are scarce and expensive.Two to three weeks from signature, with people who have shipped this before.
Cost shapeFixed monthly cost plus visa, insurance, end-of-service and equipment, whether or not there is a full workload.Scoped to the work. Costs stop when the work does.
Breadth of skillOne or two specialisms per hire. Anything outside them gets improvised or outsourced anyway.Design, engineering, security and growth from the same team, without a handoff between vendors.
Institutional knowledgeStays in the building — which is the real advantage, and the reason to hire eventually.Documented and handed over. We write runbooks so you can take it back in-house.
When it is the wrong choiceRarely, once the workload is genuinely full-time and permanent.If you need someone in your standups every day for years, hire. We will say so.

Related work

Questions people ask before starting

Yes. We build on regional deployments where residency is required, and we check the whole chain — warehouse, BI tool, extract caches and any managed connector — rather than the warehouse alone. If a tool cannot meet the requirement, we tell you before it is in the architecture.

Services that pair with this

Most engagements use two or three together.

Ready to talk about Data & Analytics?

Tell us what you are building and where it is stuck. We will come back with a scope, a timeline and a number — usually within two working days.

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