Make data and AI useful
Dashboards and reports built on numbers you can trust
We build executive and operational dashboards on governed metric definitions, so reports are consistent and support decisions rather than only displaying data.
In short
Reporting and dashboards turn operational and financial data into information people can use. We build data marts, a semantic layer of agreed metric definitions, and dashboards for executives and teams, with automated delivery and role-based access. The emphasis is on trusted metrics and consistent business definitions.
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01 The problem
What problem does this solve?
Different teams calculate the same figure in different ways, so a meeting starts with an argument about whose number is right. Reports are rebuilt by hand each period from exports and spreadsheets.
A dashboard is only as useful as the definitions under it. If revenue or margin means something different to each team, a better chart does not help. The definitions have to be agreed and governed first.
02 What we build
What do we build?
- Data marts and analytical models for your reporting questions
- A semantic layer with governed metric definitions
- Executive and KPI dashboards
- Self-service analysis for managers and analysts
- Data quality checks behind the figures
- Automated report delivery
- Role-based access control on reports and the data beneath them
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
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Agree the questions and metrics
We list the decisions the reports support and agree one definition for each metric, with a named owner for it.
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Model the data
We build data marts and quality checks so that the figures reconcile to the source systems.
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Define the semantic layer
We encode the agreed definitions once, so every dashboard uses the same business terms.
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Design the dashboards
We design views for executives, managers and analysts around their decisions, then test them with the people who will use them.
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Automate and maintain
We schedule report delivery, monitor refreshes and keep the definitions governed as the business changes.
05 Capabilities and stack
Which capabilities and technologies are involved?
Capabilities
- Power BI
- Tableau
- Looker
- Amazon QuickSight
- Apache Superset
- Grafana
- SQL
- DAX
- Power Query
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
A leadership team receives two versions of the same figure from finance and operations, and analysts spend much of each reporting period lining them up.
We agree the definitions with both teams, model the data once, and build dashboards on those definitions with scheduled delivery.
07 Questions
Questions people ask
The tool should follow your needs, existing licenses and skills. We compare the options on your requirements and total cost of ownership, and the choice is yours to make with that comparison.
Self-service analysis is part of the design. It runs on governed definitions, so what people build stays consistent with the figures leadership sees.
Not necessarily. If the data you need sits in one or two systems, a focused mart and dashboards may be enough. A broader platform fits when many teams and uses share the same information.
09 Terms
Which terms come up here?
If this sounds like you: Put dashboards your customers trust inside your product
If this sounds like you: Stop reconciling spreadsheets and get one set of numbers