Power BI Charts Your Detroit Data; It Won't Reconcile a Plant That Runs on Five Systems
Custom business intelligence work for a Detroit manufacturer runs $40k to $130k over 3 to 6 months. Tableau, Power BI, and Looker draw beautiful charts. They assume clean, reconciled data, which a plant running ERP (Enterprise Resource Planning), MES, EDI, quality, and spreadsheets does not have, so the dashboards look authoritative while quietly disagreeing with each other and with the floor.
The hard part of BI in a Detroit plant is not the chart, it is the data underneath. OEE lives in the MES, scrap in a quality system, schedule attainment in the ERP, shipping performance in the EDI logs, and three KPIs in spreadsheets. Point Power BI at each and you get five dashboards that each look right and none of which agree, because nobody reconciled the definitions of a good part, a downtime minute, or an on-time ship.
So executives get a dashboard that says 92% OEE while the plant manager knows it was a rough month, and trust evaporates. The expensive lesson is the capital decision made on a number that turned out to be a join error between the MES and the ERP. Real BI for a plant is a data model and reconciliation layer first, and a chart second.
- Your dashboards disagree and executives no longer trust them
- KPI definitions differ across ERP, MES, and quality systems
- A decision was made on a number that turned out to be a join error
- Spreadsheet KPIs and official dashboards tell two different stories
- Your data already lives clean in one ERP with consistent definitions
- You need simple charts on a single tidy source
- Power BI on your warehouse already serves your needs
- You have under $35k and reconciliation is not the problem
- A unified data model with agreed KPI definitions, so every dashboard tells one story
- A reconciliation layer that catches MES-to-ERP join errors before they reach a chart
- Plant KPIs (OEE, scrap, schedule attainment, on-time ship) trusted enough for capital decisions
- Spreadsheet metrics pulled into the official model, ending the two-truths problem
- Self-serve dashboards on a clean foundation, so analysts stop hand-building reports
- The data-engineering work is the cost; a pretty dashboard on dirty data is cheaper and worthless
- It requires agreeing on definitions across departments, which is organizational, not just technical
- Source-system changes mean pipeline maintenance you own
- If your data is already clean in one ERP, off-the-shelf BI may be enough
Business Intelligence Dashboards pricing in Detroit: the real numbers
| Project scope | Typical cost | Timeline |
|---|---|---|
| Data model + reconciliation for core KPIs | $40k to $65k | 3 to 4 months |
| Multi-source integration + executive dashboards | $65k to $95k | 4 to 5 months |
| Full warehouse + self-serve + data-quality alerting | $95k to $130k | 5 to 6 months |
The features that matter for Detroit
Detroit business intelligence dashboards: the full scope
The engagements Detroit teams bring us most often: Looker, real-time analytics, KPI dashboards, data warehouse, embedded analytics, business intelligence dashboards and BI development.
Exactly what you get
Dashboards you can actually bet capital on, because the work underneath them is reconciliation, not decoration. A unified model pulls ERP, MES, EDI, quality, and the stray spreadsheets into one place with agreed definitions, a data-quality layer catches the join errors that used to produce a fake 92% OEE, and every KPI drills down to the source records behind it. The plant manager and the executive finally read the same number.
How to choose a developer in Detroit
Hire a data engineering team, not a dashboard decorator. Ask how they reconcile MES and ERP definitions and catch join errors. The best builds sit on top of your ERP, your internal tools, and your inventory management software, turning their data into one trusted model rather than five charts that disagree.
From kickoff to launch: the schedule
- !They lead with chart design; ask how they reconcile data across systems
- !No data-quality checks; ask how they catch a join error before it ships
- !They ignore KPI definitions; ask how OEE is defined across MES and ERP
- !No drill-down to source; ask how a user verifies a suspicious number
- !Fixed quote without auditing your sources; ask for paid discovery on data quality
Most Detroit 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
How much do custom BI dashboards cost in Detroit?
Expect $40k to $130k. A data model with reconciliation for core KPIs starts near $40k to $65k. Multi-source integration with executive dashboards runs $65k to $95k, and a full warehouse with self-serve and data-quality alerting reaches $130k.
Why aren't Power BI or Tableau enough?
They visualize well but assume clean, reconciled data. A plant running ERP, MES, EDI, quality, and spreadsheets has none, so charts on each look authoritative yet disagree. The real work is the data model and reconciliation underneath.
What does reconciliation actually mean here?
It means agreeing on definitions, what counts as a good part, a downtime minute, an on-time ship, and building a layer that catches join errors between systems, so a dashboard cannot show a number that contradicts the floor.