Your Burnaby leadership reads three Power BI tabs because no dashboard speaks both film and fuel cells
Custom business intelligence dashboards for a Burnaby operation run $45,000 to $120,000 over 3 to 7 months. Tableau, Power BI, and Looker are powerful, but their value collapses when the underlying data is messy and the questions are domain-specific: a film slate's cost-to-complete, a fuel-cell line's yield, and a research program's grant burn don't share a schema, and getting them into one trustworthy view is the actual work. Custom BI is less about prettier charts and more about the data pipeline and modelling that make a Power BI or a custom dashboard tell the truth.
You bought Power BI or Tableau expecting one view of the business, and instead you have three disconnected dashboards and a nagging doubt about whether any of the numbers reconcile. The film side reports cost-to-complete one way, the manufacturing side reports yield another, and the research side tracks grant burn in its own spreadsheet, and stitching them into a single, trustworthy executive view defeats the off-the-shelf tool because the data was never modelled to fit together.
That's the misunderstood part of BI. The dashboard is the easy 20 percent; the hard 80 percent is the pipeline, the cleaning, the modelling, and the definitions that make a number mean the same thing across film, manufacturing, and research. Tableau and Looker assume you've already solved that. A Burnaby operation spanning genuinely different domains hasn't, so the dashboards look impressive and quietly disagree, and leadership learns not to trust them.
What business intelligence dashboards costs in Burnaby
| Project scope | Typical cost | Timeline |
|---|---|---|
| Data pipeline plus dashboards for a single domain | $45k to $70k | 3 to 5 months |
| Multi-domain BI with unified model and exec view | $85k to $120k | 5 to 7 months |
| Data-modelling and pipeline cleanup feeding existing Power BI | $40k to $65k | 3 to 4 months |
The fix: business intelligence dashboards built for Burnaby, not rented
You go custom on BI when the data pipeline and modelling are the real problem, which they almost always are. A build for a Burnaby operation invests in the pipeline that cleans and unifies film, manufacturing, and research data, defines metrics consistently, and then surfaces them, whether in a custom dashboard or a well-modelled Power BI. The case is trust: leadership can act on numbers that reconcile across domains, instead of three dashboards that each tell a partial, slightly different story. You're buying a trustworthy single source, not a chart library.
- Your data spans genuinely different domains that don't share a schema
- Off-the-shelf dashboards quietly disagree and leadership has stopped trusting them
- Key numbers like grant burn live in spreadsheets outside the BI tool
- You need one reconciled executive view, not three partial ones
- Your data already lives in one clean, well-structured system
- A stock Power BI or Tableau setup gives you trustworthy views
- Your metrics are standard and consistently defined
- You don't have messy multi-domain integration to solve
The capability list that earns its budget
Business Intelligence Dashboards services we deliver in Burnaby
The engagements Burnaby teams bring us most often: business intelligence dashboards, BI development, data visualization, Tableau alternative and Power BI.
How long it takes, phase by phase
Exactly what you get
A trustworthy BI layer where the real investment is the pipeline: film, manufacturing, and research data cleaned, unified, and consistently defined, then surfaced in an executive view with drill-down. It pulls from the production-cost ERP (Enterprise Resource Planning), the inventory management software on the line, the accounting software closing the books, and the spreadsheets holding grant burn, so leadership reads one reconciled picture instead of three that disagree.
How to choose a developer in Burnaby
Hire a team that talks about data pipelines and metric definitions before chart types, because that's where BI succeeds or fails. Ask how they'll make a number reconcile across film, manufacturing, and research, and how they'll pull grant spend out of spreadsheets. Burnaby's data-rich mix of studios, manufacturers, and research institutions means local developers can handle genuinely multi-domain data. Be wary of anyone who demos beautiful dashboards without asking hard questions about your source data.
- A data pipeline that unifies film, manufacturing, and research sources into one trustworthy model
- Consistent metric definitions across domains, so the dashboards stop quietly disagreeing
- Grant and research spend pulled into the BI layer instead of stranded in spreadsheets
- A single executive view leadership can actually act on, with drill-down to each domain
- Automated, current data instead of a manual monthly stitch-together
- Most of the cost and time goes into invisible pipeline work, which can feel like paying for nothing visible
- Dashboards need ongoing care as sources change, so it's a living system, not a one-time build
- Garbage in still means garbage out; if source data is bad, BI exposes it rather than fixing it
- If your data already lives in one clean system, a stock Power BI setup may be all you need
- !They lead with chart design; ask how they'll unify and clean your cross-domain data first
- !No metric-definition plan; ask how the same number means the same thing across film and manufacturing
- !They ignore your spreadsheets; ask how grant burn reaches the dashboard
- !They promise dashboards in two weeks; ask what they're skipping in the pipeline
- !No refresh or maintenance plan; ask how the numbers stay current as sources change
Most Burnaby 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 do our Power BI dashboards disagree with each other?
Because the underlying data was never modelled to fit together. When film, manufacturing, and research each define metrics their own way and live in separate systems, dashboards built on top quietly diverge. The fix isn't prettier charts, it's a data pipeline and a consistent semantic model, which is the hard, invisible 80 percent of BI that off-the-shelf tools assume you've already done.
Isn't most of a BI project just building charts?
No, that's the easy part. The bulk of the work and the cost is the data pipeline, ingesting, cleaning, and unifying sources, and defining metrics so they mean the same thing everywhere. Tableau and Power BI make the charts easy; they don't solve your messy, multi-domain data, which is exactly why a Burnaby operation spanning film and manufacturing needs custom pipeline work.
Can custom BI fix bad source data?
It exposes bad data and can clean and reconcile it in the pipeline, but it can't invent quality that isn't there, garbage in still produces garbage out. A good build surfaces data-quality problems early so you can fix them at the source, which is often a valuable side effect: leadership finally sees where the numbers were never trustworthy.
Do we need custom dashboards or just better Power BI?
Often the answer is better-modelled Power BI, fed by a proper pipeline, rather than a fully custom dashboard front-end. If your data already lives in one clean system, stock Power BI may suffice. The custom investment is justified by messy, multi-domain integration, not by the dashboard layer itself, which is why a good developer may keep Power BI and rebuild what's underneath.
How do we keep the dashboards trustworthy over time?
Through automated refresh and ongoing maintenance, because sources change and a BI layer is a living system. A one-time build that nobody maintains drifts out of date and back into distrust. Budget for the pipeline to be cared for, and the single reconciled view stays current and credible instead of decaying into another set of stale tabs.