Since 2010
Building Software
One Version of the Numbers.
Data Analytics Solutions and Services
We build the analytics layer your teams argue with instead of about: reliable pipelines, a modelled warehouse, and self-serve dashboards with agreed metric definitions, delivered with fixed pricing and full handover.
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 3,900+
- Apps Published
- 500+
- Engineers
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
Why analytics projects fail, and how we avoid it
Most analytics failures are not visualisation failures; they are plumbing and definitions. Numbers disagree between reports because the pipelines are fragile and every team defines the metric differently. We fix that layer first: reliable ingestion, tested transformations, and one agreed definition per metric, then build dashboards on top.
The result is analytics your team operates without us: documented models, tested data, and dashboards that refresh themselves. For analysis and modelling on top of this layer, see our data science services.
What our data analytics services cover
The layers between raw sources and decisions people trust.
Data pipeline engineering
Reliable ingestion from your product, billing, and marketing sources with monitoring, so dashboards are fresh and trustworthy.
- ✓Batch and streaming ingestion
- ✓Source change handling
- ✓Pipeline monitoring
- ✓Backfill tooling
Warehouse and data modelling
A modelled warehouse with tested transformations, so every metric has one definition and one owner.
- ✓Dimensional modelling
- ✓Transformation frameworks
- ✓Data tests
- ✓Documentation generation
Self-serve dashboards
Dashboards built around your team's recurring questions, refreshable and filterable without an analyst in the loop.
- ✓Role-based views
- ✓Scheduled refreshes
- ✓Drill-down paths
- ✓Metric tooltips and definitions
Marketing and product analytics
Funnel, cohort, and attribution views wired to the product events and campaigns that actually drive them.
- ✓Event tracking design
- ✓Funnels and cohorts
- ✓Attribution modelling
- ✓Campaign reporting
Operational reporting
Automated reports and alerts on the operational metrics your teams manage by the week.
- ✓Automated delivery
- ✓Threshold alerting
- ✓SLA reporting
- ✓Exception views
Analytics rescue
Fragile spreadsheets and contradictory dashboards consolidated into one trusted, tested platform.
- ✓Spreadsheet consolidation
- ✓Metric reconciliation
- ✓Pipeline rebuild
- ✓Migration cutover plans
What every engagement includes
Every data analytics project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped data analytics roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All data analytics source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with data analytics
Assets our clients rely on weekly, all handed over documented.
Unified analytics warehouse
One modelled source of truth combining product, billing, and marketing data with tested transformations.
Executive KPI dashboard
A leadership dashboard with agreed definitions behind every number, refreshed automatically.
Product analytics stack
Event tracking, funnels, and cohort views that show what features do to retention and revenue.
Marketing attribution
Channel and campaign reporting your growth team trusts enough to reallocate spend against.
Ops and finance reporting
Operational and financial metric reporting with alerting when thresholds drift.
How a data analytics Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your analytics goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The data analytics architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. data analytics work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged analytics launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our analytics builds
Default choices, stated with reasons.
Ingestion
Managed connectors for standard SaaS sources and custom pipelines for your own systems, with schema-change handling built in.
Warehouse
A modern cloud warehouse sized to your volume, with cost controls and access policies managed as code.
Transformation
Version-controlled transformation frameworks with automated tests, so a broken metric fails a test rather than a meeting.
BI layer
Your existing BI tool where it serves; we replace it only with documented reasons, and always ship metric documentation.
How intelligence flows through the platform
What each layer reads, and what it changes.
Ingestion layer
Moves source data into the warehouse with change detection.
Reads
Product, billing, and marketing sources
Changes
Raw data tables
Modelling layer
Tests and transforms raw data into defined metrics.
Reads
Raw tables, test definitions
Changes
Modelled metric tables
Delivery layer
Serves dashboards, alerts, and reports from the models.
Reads
Modelled tables, access rules
Changes
Dashboards and alert states
What's included, standard
The baseline on every data analytics engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each data analytics release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Metric definition workshop
A working session where your teams agree one definition per key metric, documented for the warehouse.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for data analytics
The differences live in engineering habits, not pitch decks.
Definitions before dashboards
We facilitate one agreed definition per metric before building anything, which is the step that ends the recurring arguments.
Tested pipelines
Data quality tests run on every pipeline update, so a broken number fails a test instead of surfacing in a board meeting.
Built for Self-serve
Dashboards are designed so your teams answer their own questions, with analysts freed for real analysis.
Documented handover
Models, pipelines, and definitions ship with documentation, so your team owns the platform from day one.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with analytics platforms among them.
Data analytics services FAQs
$ moxace launch --nowReady for One Version of the Numbers?
Talk to our analytics team about a fixed-scope platform build with tested pipelines and full handover.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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