Business Intelligence Dashboards · Stoke-on-Trent

Power BI shows a healthy quarter while one heritage range loses money on every firing

The short answer

A custom BI dashboard build for a Stoke-on-Trent operation runs $30k to $85k over 2 to 5 months. You commission it when Tableau, Power BI or Looker can chart your data but can't model firing-level margin, grading yield or marketplace SLA performance, so the dashboards show healthy averages while specific ranges and channels quietly lose money.

Tableau, Power BI and Looker are powerful, but a dashboard is only as good as the model behind it. Point them at a Potteries firm's data and they'll happily chart revenue and a flattering overall margin, while averaging away the fact that one beloved heritage range loses money on every firing once you account for energy and yield loss. The default dashboards answer the easy questions and bury the expensive ones.

For the city's fulfilment operators, the blind spot is operational: a generic dashboard shows total orders shipped but not SLA adherence by carrier, breakage rates by range, or which pick paths cost the most. The data exists, but without a model that understands firings, grades and fulfilment, the BI tool produces pretty charts that don't tell you where the money is actually going.

The fix: business intelligence dashboards built for Stoke-on-Trent, not rented

A custom BI build is really a custom data model: it pulls firing costs, grading yield, energy allocation and SLA data into one place and surfaces margin per range, per kiln and per channel, plus the operational metrics fulfilment needs. The dashboards stop flattering you and start showing where money leaks, which range to retire, and which carrier misses cut-offs. That decision-grade modelling is what off-the-shelf BI defaults can't give you.

The capability list that earns its budget

What to build in
+Custom data model unifying firing, grading, energy and sales data
+Margin analysis by range, kiln and channel
+Marketplace SLA and carrier-performance dashboards
+Breakage and mispick tracking by range
+Automated pipelines from ERP (Enterprise Resource Planning), inventory and fulfilment systems
+Role-based dashboards for floor, office and leadership

What we build under business intelligence dashboards in Stoke-on-Trent

Everything a business intelligence dashboards build here can cover: real-time analytics, KPI dashboards, data warehouse, embedded analytics, business intelligence dashboards and BI development.

What business intelligence dashboards costs in Stoke-on-Trent

Project scopeTypical costTimeline
Custom model with core dashboards$30k to $50k2 to 3 months
Full margin and SLA analytics suite$50k to $85k3 to 5 months
Multi-site BI platform with pipelines$85k+5 to 8 months
Cost by project scopeCost by project scopeCustom model with core dashboards$30k to $50kFull margin and SLA analytics suite$50k to $85kMulti-site BI platform with pipelines$47k to $85k
Typical project cost bands. Source: Digital Heroes 2026 delivery benchmarks.

How long it takes, phase by phase

Delivery timeline by phaseDelivery timeline by phaseDiscovery2 wkDesign2 wkBuild6 wkTest1 wkLaunch1 wk
Indicative delivery timeline by phase.
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Exactly what you get

You get dashboards built on a model that understands your business, not just your spreadsheets. Firing costs, grading yield, energy allocation and SLA data flow into one place, and the dashboards show margin per range, per kiln and per channel, plus the breakage and carrier-performance metrics fulfilment needs. The loss-making heritage range stops hiding behind a healthy average. The pipelines draw from your custom ERP, inventory management system and warehouse management system so the numbers stay current.

How to choose a developer in Stoke-on-Trent

Hire a developer who spends most of the conversation on the data model, not the chart colours. The value of BI for a Potteries firm is a model that knows firings, grades and SLAs; the visuals are the easy part. Ask how they'll surface margin per range, how they handle messy source data, and how the pipelines keep dashboards current. A team that understands kiln economics and corridor fulfilment will model the metrics that actually move your business.

The benefits
  • Margin per range, per kiln and per channel, not a flattering average
  • Firing cost, energy and yield loss surfaced where decisions are made
  • Marketplace SLA adherence and breakage rates by carrier and range
  • A model that turns existing data into clear actions
  • Dashboards leaders actually trust because the numbers are real
The trade-offs
  • The value is in the data model, which takes effort beyond pretty charts
  • Dashboards are only as good as the data feeding them, so cleanup is needed
  • You maintain the pipelines as source systems change
  • If your data is clean and standard, Power BI defaults may suffice
Red flags when hiring (and what to ask instead)
  • !They focus on chart looks, not the model; ask what data model sits behind the dashboards
  • !No firing-margin modelling; ask how a loss-making range surfaces
  • !No SLA or breakage metrics; ask how fulfilment performance is shown
  • !They ignore data quality; ask how they handle messy source data
  • !No pipeline plan; ask how dashboards stay current as systems change

Most Stoke-on-Trent teams pricing business intelligence dashboards end up comparing notes on helpdesk & ticketing, erp, custom software too; the systems share one data spine.

Rohan Malhotra · Enterprise Software Consultant

Rohan advises mid-market and enterprise teams on ERP, CRM and custom software, and has led delivery on dozens of business-software builds.

Writes for Digital Heroes, shipping business software for 2,000+ brands across 55+ countries since 2017.

FAQ

Frequently asked questions

Why do our Power BI dashboards look fine while we lose money?

Because the default model averages everything into a flattering total. A heritage range that loses money on every firing, once energy and yield loss are counted, disappears inside the overall margin. A custom model breaks the numbers down per range, per kiln and per channel so the loss finally shows.

Isn't BI just about pretty charts?

No, the charts are the easy 20 percent. The value is the data model behind them: one that understands firing costs, grading yield and SLA performance and pulls them together correctly. A beautiful dashboard on a weak model just lies more attractively. Spend the budget on the model.

What if our data is messy?

Then cleaning and modelling it is part of the project, and a good developer is upfront about that. Dashboards are only as good as the data feeding them, so the build includes the pipelines and the cleanup. Anyone promising instant dashboards on messy source data is overselling.

Can it track our fulfilment SLAs?

Yes. For the city's fulfilment operators, custom BI surfaces SLA adherence by carrier, breakage rates by range, and pick-path costs, none of which generic order-count dashboards show. That operational visibility is often where the fastest savings hide, because missed cut-offs and breakages add up quietly.

How do dashboards stay up to date?

Through automated pipelines from your source systems, your ERP, inventory and fulfilment tools, so the dashboards refresh without manual exports. Part of the build is wiring those pipelines reliably. Without them, dashboards drift out of date and people stop trusting them, which defeats the purpose.

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