Analytics and Business Intelligence
Answers from your data in plain language, and dashboards that show what needs a decision today.
Why It Matters
Executives ask a simple question and wait days for an analyst. Dashboards multiply, each with its own numbers, and few show what needs attention now.
When a measure has no agreed definition, a report, a dashboard and an AI assistant can give three answers. Trust falls, and people return to the spreadsheet they keep on the side. Three failures are common. A dashboard is built before the decision is known, a definition lives in one query, and access rules arrive after users.
What We Do
Dashboards and Command Centres
A short set of measures for leaders, updated from the source and showing what needs a decision today.
Conversational Analytics
People ask in plain words and get an answer with the query and data behind it, so the numbers can be checked.
Automated Reporting
Reports that build and send themselves on the schedule you set, from the same agreed definitions as the dashboards.
Analytics Inside Your Products
Charts and measures inside your product, with each customer limited to their own data.
What Changes for You
Questions in Plain Language
Business users ask in their own words and get an answer they can trace to the query and the data.
Decisions in View
A short set of measures shows what needs a decision today, with an alert when a value moves outside its range.
Analytics Your Customers Use
The same measures appear inside your products, so customers and partners see their own data.
How the Work Runs
The work starts from decisions, not from a tool. One agreed definition serves every dashboard, report and answer.
Start From the Decisions
We agree the decisions, questions and users that matter most, and the measures that support them. You name the owner of each measure.
Agree the Definitions
We put the measures in one shared set of definitions, so every screen and every answer reads the same rule. Your finance and operations owners sign off the definitions.
Set the Access Rules
We apply row and role rules to every dataset and test them with real users. Your data owners approve who sees which rows before release.
Build and Test With Users
We build the first dashboards and question sets, and test answers against figures your teams already trust. Users try them on their own work before release.
Release and Extend
We release to a first group, review what they ask and where answers fall short, then widen access. You decide the order in which teams and products are added.
Widen the Use
We add teams and products in the order you choose, and review the questions they ask so the shared definitions cover real work.
Platforms and Technologies
Analytics sits on the data platform you already run and the tools your teams can use. Microsoft and Google Cloud provide the platform for the models and the shared definitions, and Salesforce, SAP and Oracle supply the business data. Microsoft Power BI and Tableau are the reporting tools we build in; SQL is the query language under both. A semantic layer is the shared set of definitions under every report. It holds each measure once, so a new dashboard or answer reads it rather than copying a query. Each answer shows its query, so a reader can check how it was made.
- Languages
- SQL
- Data and analytics
- Power BI
- Tableau
- Methods We Use
- Conversational analytics
- A shared semantic layer
- Row and role level access
- Analytics inside a product
Naming a product does not mean a partnership with its vendor.
Microsoft
We join Azure, Microsoft 365 and Dynamics 365 on one identity model, so teams share one set of records.
Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
Salesforce
We build Salesforce around the agreed customer process, with clean data and every integration owned.
SAP
We move SAP to S/4HANA, connect it to banks and tax platforms, and keep the core clean with work on BTP.
Oracle
We move Oracle databases and Fusion applications together, with performance tested before each cutover.
Industries
Logistics and Distribution
Warehouse, transport and carrier integrations, tracking and proof of delivery, joined to your ERP and finance.
Public Sector
Citizen portals, case management, payments and integrations with government registries, designed to recognised accessibility guidelines.
Retail and Omnichannel Commerce
POS integration, stock and price sync, card payments, loyalty and e-commerce for retailers with many sites.
Questions
What buyers ask before analytics reaches the people who decide.
Can we trust an answer from a conversational tool?
Only if it can be traced. Every answer shows the query it ran and the data it used, reads from agreed definitions and is tested against figures your teams already trust.
Will users see data they should not?
No. The same row and role rules apply to dashboards, reports and AI answers, so each person sees what their role allows.
Do we have to replace our current reporting tools?
Not necessarily. We can add shared definitions and conversational analysis on top of what you have, then retire overlapping dashboards as their users move.
Can we offer analytics to our own customers?
Yes. We build it into your products, with each customer limited to their own data. Where the data has wider value, we can package it as a product customers explore themselves.
What if our data is not ready?
Then we fix the foundation first. We start with the sources, quality checks and definitions the reports need, and add the reporting when the numbers hold.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.