Your Power BI dashboard looks great and means nothing, because your Middlesbrough production, projects and finance each report a different number
A genuinely useful BI dashboard build for a Middlesbrough firm typically costs £25k to £90k and takes 2 to 5 months. Tableau, Power BI and Looker are powerful visualisation layers, but they're only as good as the data beneath them. For a Teesside firm with production, project, inventory and finance data in separate systems that disagree, the hard part isn't the chart, it's the plumbing.
You bought Power BI and got beautiful charts that the board doesn't trust, because production says one output figure, the project system says another margin, and finance reconciles neither until month-end. The dashboard isn't wrong about the data it has; it's that the data lives in five places that were never joined, so every number invites an argument.
The visualisation tool can't fix that. Without a clean, reconciled data layer underneath, a BI dashboard just renders the disagreement faster and prettier, and people go back to their own spreadsheets to get a number they believe.
Why the usual tools struggle in Middlesbrough
- Power BI charts are only as trustworthy as the scattered data behind them, which doesn't agree
- Production, project, inventory and finance systems define the same metric differently
- Without a reconciled data layer, every dashboard number sparks a debate
- Manual exports to feed dashboards mean numbers are stale by the time they're seen
What a custom business intelligence dashboards build changes
A real BI build is mostly the data layer: pipelines that pull from your production, project, inventory and finance systems, reconcile definitions, and feed a single trusted model the dashboards sit on. The charts are the easy last step. You get numbers the board argues from, not about.
- Your data lives in several systems that disagree and dashboards aren't trusted
- The same metric is defined differently across production, projects and finance
- Manual exports make reporting stale and labour-intensive
- You need one trusted set of numbers for board decisions
- Your data already lives cleanly in one system
- Off-the-shelf Power BI connectors cover your sources well
- You have light reporting needs a standard tool meets
- You're not ready to reconcile conflicting definitions yet
- A reconciled data layer so one definition of output, margin and stock is shared across the firm
- Automated pipelines that keep dashboards current instead of stale exports
- Numbers the board trusts enough to make decisions from
- Self-serve dashboards for production, projects and finance off one model
- A foundation that future systems and AI can build on
- Most of the cost is unglamorous data engineering, not the visuals people imagine
- Reconciling conflicting definitions forces decisions teams have avoided
- The model needs maintaining as source systems change
- If your data is already clean in one system, off-the-shelf BI may be enough
The features that matter for Middlesbrough
Middlesbrough business intelligence dashboards: the full scope
The engagements Middlesbrough teams bring us most often: real-time analytics, KPI dashboards, data warehouse, embedded analytics, business intelligence dashboards, BI development and data visualization.
Business Intelligence Dashboards pricing in Middlesbrough: the real numbers
| Project scope | Typical cost | Timeline |
|---|---|---|
| Dashboards on an existing clean data source | £8k to £20k | 3 to 6 weeks |
| Custom data layer plus dashboards across systems | £35k to £65k | 2 to 4 months |
| Full BI platform with pipelines and alerting | £65k to £90k | 4 to 5 months |
From kickoff to launch: the schedule
Exactly what you get
Dashboards the board trusts, because the work went into the data layer beneath them. Pipelines pull from your production, project, inventory, CRM and finance systems, reconcile what each calls output, margin and stock, and feed one model the dashboards sit on. Numbers refresh automatically instead of via stale exports, you can drill from a headline KPI to the source transaction, and thresholds raise alerts. The result is one set of figures people argue from, not about.
How to choose a developer in Middlesbrough
Hire for data engineering, not dashboard design. The trap is a partner who demos gorgeous visuals and glosses over the plumbing; the value is in pipelines and reconciled definitions, which is 80% of the work. Ask how they'll join your production, project and finance data and agree one definition of margin. Expect them to connect to your ERP (Enterprise Resource Planning) software, project management software and custom CRM, and to lay a foundation future systems can build on. A team that leads with charts is selling the easy 20%.
- !They talk only about charts and colours. Ask about the data layer underneath
- !No plan to reconcile conflicting definitions. Ask how one metric is agreed
- !They assume your data is clean. Ask how they'll handle five disagreeing systems
- !No automated refresh. Ask how dashboards avoid going stale
- !No data-engineering reference. Ask for a multi-source pipeline build
Most Middlesbrough teams pricing business intelligence dashboards end up comparing notes on helpdesk & ticketing, erp, custom software too; the systems share one data spine.
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.
Frequently asked questions
Why don't our Power BI dashboards get trusted?
Because the data behind them is scattered across systems that disagree. Power BI faithfully renders whatever it's given, so if production, projects and finance define output and margin differently, the dashboard shows conflicting numbers and people retreat to their own spreadsheets. The fix is a reconciled data layer, not a better chart.
Isn't BI just about making charts?
No, and that's the costly misconception. For a Middlesbrough firm with data in five systems, roughly 80% of the work is data engineering: building pipelines, reconciling definitions and creating one trusted model. The charts are the easy final step. A partner who focuses on visuals is selling you the part that was never the problem.
What does reconciling definitions actually mean?
It means agreeing, once, what a metric is. If production counts output one way and finance another, the dashboard can't show a single trustworthy figure until you decide the definition. That forces conversations teams have avoided, which is uncomfortable but is exactly what turns pretty charts into numbers the board can act on.
How do dashboards stay current?
Through automated pipelines that refresh the data model on a schedule, rather than someone exporting CSVs by hand. Manual exports go stale fast and eat time, so automation is what keeps the dashboard reflecting reality. Build the refresh into the data layer, and current numbers stop being a manual chore.
Does this set us up for AI later?
Yes. A clean, reconciled data layer is the foundation any analytics or AI work needs. Once your production, project and finance data is joined and trustworthy, you can layer forecasting and other intelligence on top. Building BI properly now means you're not re-doing the plumbing when you want to do something smarter with the data.