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BI & Dashboard Development

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A dashboard is a decision tool, not a data dump. We design and build reporting in Power BI, Tableau, Looker Studio and Looker that starts from the decision someone needs to make, sits on a properly modelled dataset, loads quickly, and is governed so it does not fragment into forty conflicting copies within a year.

Challenges we solve

Dashboard estates decay in a predictable way. Someone asks for a view. It gets built quickly on whatever data is nearest. Someone else needs a variation and copies it. Two years later there are forty near-duplicates, four incompatible definitions of the same metric, a load time nobody tolerates, and an audience that has quietly gone back to spreadsheets. The fix is boring and effective: a modelled dataset underneath, a small number of governed dashboards designed around actual decisions, clear ownership, a request process for changes, and a retirement policy for views nobody opens. The symptom is always the same. Marketing quotes one conversion number, finance quotes another, and the meeting turns into a debate about the data instead of a decision about the business. We fix the collection layer first, prove it with test evidence, and then hand you the documentation that keeps it from drifting again.

What we deliver

Reporting strategy and inventory

A full inventory of what exists, who opens it and what decision it supports — followed by consolidation. Most estates can be reduced substantially before anything new is built, which is usually the highest-value first step.

Decision-led dashboard design

Design that starts from the question and the action, not the available columns: a clear primary metric, comparison context, drill paths that follow how people actually investigate, and deliberate restraint about what is left out.

Power BI development

Semantic models, DAX measures, incremental refresh, aggregations, row-level security, deployment pipelines and workspace governance — built for performance rather than assembled and hoped for.

Tableau development

Published data sources, extract strategy and refresh scheduling, calculated field standards, level-of-detail expressions, performance tuning and permission structures.

Looker Studio and Looker

Looker Studio reporting on BigQuery with BI Engine and extract optimisation, blended sources where appropriate, and LookML modelling for organisations that need a governed layer with version control.

Marketing and executive reporting

Cross-channel performance reporting that reconciles platform figures with the warehouse, plus executive views that lead with the answer and keep the detail one click away.

Performance and cost optimisation

Query and model tuning, aggregation strategy, extract versus live decisions, and warehouse cost control — because a dashboard nobody waits for is a dashboard nobody uses.

Data strength and Data Manager programmes

Improving match quality and durability of first-party signal: Google Data Manager onboarding, customer data ingestion, identity strategy, hashed identifier coverage, and a measurable plan for raising match rates rather than a one-off upload.

Governance, adoption and enablement

Certification of trusted reports, naming and design standards, a change request process, usage monitoring, a retirement policy and training so the estate stays healthy after handover.

Platforms & Tooling

  • Power BI
  • Tableau
  • Looker Studio
  • Looker
  • BigQuery
  • Snowflake
  • Databricks
  • dbt
  • Google Sheets

Process

Week 1

Inventory

What exists, who uses it, what decision it serves.

Week 2

Design

Decision mapping, wireframes, metric definitions agreed.

Week 3–8

Build

Models and dashboards delivered in reviewable slices.

Week 9–10

Optimise

Performance tuning, security, certification, training.

Ongoing

Govern

Usage review, change requests and retirement.

FAQs

Power BI usually wins where you are already on Microsoft and need enterprise governance at low licence cost. Tableau wins where visual analysis and exploration matter most. Looker Studio is hard to beat for marketing reporting on BigQuery at zero licence cost, and Looker proper for a governed modelling layer. We are not resellers, so the recommendation follows your stack and skills.

Often, yes. Many performance and trust problems come from the data model rather than the dashboard, so fixing the model underneath rescues the reports on top. The inventory tells us what is worth keeping.

Almost always the model: direct queries against wide unaggregated tables, row-level calculations that should be pre-computed, over-blended sources, or missing aggregation. It is rarely the visualisation layer, which is why rebuilding the front end alone tends not to help.

Certification, ownership, a request process and a retirement policy — plus usage monitoring so unopened reports get removed. Governance is the difference between a healthy estate in two years and another consolidation project.

Yes. We work to your design system, including accessible colour choices — which matters more than people expect, because a lot of default chart palettes fail contrast requirements and are unreadable for colour-blind users.

They complement each other. Dashboards are better for monitoring a known set of metrics; a conversational layer handles the long tail of one-off questions. Both should read from the same semantic model, or you are back to conflicting numbers.

Ready To Make Your Data Work Harder?

Let’s build a trusted measurement foundation that drives smarter decisions and measurable growth.

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