Data warehouse and modelling
A structured, reporting-ready data layer rather than dashboards querying production systems directly.
Business intelligence and dashboards built on accurate data
We bring the data from all your systems into one place, agree how each metric is calculated, then build the dashboards your teams use. Finance, operations, and sales all work from the same numbers.
Data platform
The problem
When every department reports a different number
Finance has a figure, operations has a different one, and the meeting is spent reconciling rather than deciding. Each is defensible, because each came from a different system with a different definition of the same thing.
Buying a BI tool does not fix that. A dashboard is a window onto whatever data it is pointed at, and pointing it at five inconsistent sources produces five inconsistent views, faster than before.
What makes reporting trustworthy is the layer underneath. Agreed definitions, consistent product and entity references, one place the numbers come from, and the ability to click into a figure and see the records behind it. Build that, and the dashboard is the straightforward part.
How it works
Foundation first, then the reporting
01
Agree what the numbers mean
Revenue, margin, stock on hand, active customer. Most disagreements are definitional rather than technical, and they get settled before anything is built.
02
Build the data foundation
Sources connected, data cleaned and standardised, and a model designed for reporting rather than for whichever system produced it.
03
Model the metrics
Definitions implemented once, centrally, so every report calculates the same measure the same way.
04
Build the reporting
Dashboards and reports for the people who will actually use them, in the tool that suits you, or one we build.
05
Keep it honest
Refreshed on schedule, monitored for gaps, and extended as the questions change. Reporting is a system you keep, not a project you finish.
What's included
Reporting your leadership can rely on
Single metric definitions
Each measure defined once and reused everywhere, so two reports cannot disagree.
Multi-entity and multi-currency
Group, country, and company views that hold up, with currency handled explicitly rather than assumed.
Dashboards for real users
Operational views for daily work and executive views for decisions, built for the people who use them.
Self-service where it helps
Filters and exploration for teams who want to answer their own questions, without opening the underlying data to accidental misuse.
Automated and scheduled reporting
Reports that arrive rather than being requested, in the format and cadence people actually need.
Traceability
Any figure can be traced to the source records behind it, which is what makes people trust it.
Forecasting and analytics
Where it is genuinely useful, predictive and statistical work built on the same foundation.
AI summaries on trusted numbers
Plain-language summaries and answers to questions, generated by AI from the same agreed definitions, so the AI and the dashboard never disagree.
Where it helps
Where reporting is holding the business back
Multi-system operations
Several platforms holding pieces of the same picture, with no consolidated view.
Multi-entity groups
Several countries or companies needing both a group view and a clean entity-level one.
Retail and commerce
Sales, stock, margin, and channel performance across online and physical, in one place.
Manual reporting cycles
Teams spending days each month building reports by hand that go stale on arrival.
Failed BI attempts
A tool was bought, dashboards were built, nobody trusts them. Usually a foundation problem, not a tool problem.
Real-world example
Three versions of last month
A multi-channel operation where each department reported from its own system and no two figures matched.
Before
- Monthly reporting took several days of manual export and merging.
- Sales, stock, and finance figures disagreed, so meetings started with reconciliation.
- Nobody could trace a number back to the transactions behind it.
After
- One data layer feeds every report, refreshed automatically.
- Metrics are defined once, so figures agree by construction rather than negotiation.
- Any number can be opened down to the source records.
What to expect
One agreed number for every metric
1
Definition per metric, used everywhere
0
Spreadsheets in the monthly close
100%
Of figures traceable to source records
1
Refresh a day at minimum
FAQ
Questions, answered
What teams ask us most about business intelligence.
AI reporting sits on top of the tools you already use and gets dashboards and summaries running quickly. Business intelligence builds the data foundation first, for businesses with several systems, entities, or currencies that need every figure to agree. If your data sits in two or three tools, AI reporting is usually the better place to start.
Keep exploring
Related solutions
Let's build
Build the system underneath it all
Book a consultation. We'll map the systems you run today, show you where the data breaks, and set out what connecting them properly looks like.
connect@codexgate.comMalaysia · Hong Kong · Singapore