Business intelligence and data visualization people actually use

Most companies do not lack reports; they lack reports anyone trusts or opens. We define the measures with the people who use them, build dashboards in Power BI and the tools you already own, and add analytics agents, so a manager can ask a question in plain English and get an answer computed from the data.

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What we cover

How we workFrom your data to a dashboard people open
DefinitionsKPIs everyone agrees on
Data visualizationCharts chosen for the question, not for decoration
ToolsPower BI first, and the tools you already own
Analytics agentsAsk the data a question, get a computed answer
Self-serviceSelf-service analytics without the sprawl

How we work

From your data to a dashboard people open

A business intelligence project goes wrong when it starts with charts. Ours starts with the data you have and the decisions it should support. We review your current data and reports, agree the requirements with the people who will use them, and choose the approach that fits: reporting directly from a clean source, or a data warehouse when the data comes from several systems. Then we gather and cleanse the data, build the model and the reports, and test them with real users before the rollout.

What we do

Review of current data and reports
Requirements agreed with report users
Data preparation and modeling
Testing with real users before rollout

Definitions

KPIs everyone agrees on

If sales and finance calculate revenue differently, a dashboard only makes the argument faster. We write down each measure with its owner: what it counts, what it leaves out, which system it comes from and how often it refreshes. The definitions live in one shared semantic model, so every report that shows revenue shows the same revenue, and a change to a definition is made once.

What we do

A data dictionary for every measure
One shared semantic model
Owners who approve definition changes
Row-level security by role

Data visualization

Charts chosen for the question, not for decoration

Good data visualization makes the important thing obvious. We choose each visual for the question it answers: a line chart for a trend, a bar chart to compare, a map when location matters, a table when people need the exact figure, and a pivot table or a heat map where two dimensions meet. Each dashboard opens on the few numbers its audience checks every day, with filters and drill-through for the detail, and it works on a phone as well as on a large screen.

What we do

Executive, operational and self-service dashboards
Drill-through from summary to detail
Accessible colors and labels
Mobile layouts for people on the move

Tools

Power BI first, and the tools you already own

Most of our BI work is in Microsoft Power BI, which suits companies on Microsoft 365 and works with Microsoft Fabric, Teams and SharePoint. We also work with Tableau, Qlik, Looker and Amazon QuickSight, and if you already have licenses and trained users in one of them, we will usually build there rather than start again. Moving reports from one tool to another is a project we plan like any migration: an inventory, priorities, and the old and new reports compared before the old one is retired.

What we do

Power BI reports, semantic models and workspaces
Tableau, Qlik, Looker and Amazon QuickSight
Report migrations between tools
Governance of workspaces and sharing

Analytics agents

Ask the data a question, get a computed answer

Dashboards answer the questions you knew to ask. For the rest, we build analytics agents in Teams and Copilot: a manager types a question, and the agent runs the analysis and returns a chart or a report. For a national food cooperative, marketing staff ask about paid-media results in plain English. The agent reads campaign data from SharePoint, runs the calculations in Python on Azure Functions rather than summarizing, and returns charts and Word or Excel reports, with secure access through a managed identity and no stored passwords.

What we do

Analytics agents in Teams and Copilot
Calculations done in code, not guessed by the model
Charts, Word and Excel reports from a chat
Access through managed identities

Self-service

Self-service analytics without the sprawl

Self-service analytics lets people answer their own questions, but without guardrails it produces many versions of the same report. We set up shared, approved data sets that everyone builds on, workspaces with clear owners, and short training for the people who build reports, so self-service grows on trusted data. Usage reports then show which dashboards are used and which can be retired.

Case studies

BI and data visualization in practice

Client names are withheld. We are happy to share details on a call.

All case studies

Delivered
KPIs on request
Food manufacturing
Data Analytics
Marketing performance agent for a national food cooperative
Marketing staff ask about paid-media results in plain English. The agent reads campaign data from SharePoint, runs the calculations in Python on Azure Functions rather than summarizing, and returns charts and Word or Excel reports.
- Campaign KPIs without waiting for an analyst
- Secure access with managed identity, no stored passwords
Read the case study →

Questions

Common questions

What is the difference between business intelligence and analytics?

Business intelligence usually means reporting on what has happened and what is happening, through dashboards and reports on agreed measures. Analytics goes further: finding out why, forecasting what is likely and testing options. We cover both, and analytics agents bring the second within reach of people who do not write queries.

Do we need a data warehouse before we can use Power BI?

Not always. If your data comes from one or two clean sources, Power BI can report on them directly. Once reports combine several systems or need a long history, a warehouse or a Fabric lakehouse makes them faster and more consistent.

Can you fix the dashboards we already have?

Yes. We review the existing reports and models, consolidate duplicate measures, speed up slow reports and retire what nobody uses, and then hand them back or keep supporting them.

Are analytics agents accurate?

They are when the calculation is done in code on governed data, rather than left to the language model, which is how we build them. The model handles the conversation; the numbers come from the data.

From the blog