Your San Diego dashboards look great and cannot answer the one question that matters
Custom BI and data dashboards for a San Diego organization run $45k to $130k over 2 to 6 months. The win is a unified data layer underneath the dashboard, joining your LIMS, ERP (Enterprise Resource Planning), and grant data, instead of Tableau sitting on top of disconnected sources that cannot answer burn-per-grant or cost-per-program without a manual export.
Tableau, Power BI, and Looker are excellent visualization layers and only as good as the data underneath them. The real San Diego problem is that the data lives in islands: experiment results in a LIMS, spend in an ERP, grant budgets in a spreadsheet, sample inventory in another tool. A pretty dashboard cannot answer burn rate per grant against milestones because the numbers never come together in one place.
So someone exports from four systems, joins them in Excel, and feeds a static dashboard that is stale the moment it is built. Leadership makes decisions on last month's stitched-together numbers, and the BI tool that promised real-time insight is really just a nicer-looking version of the same spreadsheet problem the org already had.
- Your key questions span systems that do not join, so answers require manual exports
- Leadership is deciding on stale, hand-stitched numbers
- You have multiple data islands and no shared layer beneath the dashboard
- Your data already lives in one clean system and Tableau on top is enough
- Your reporting needs are within a single source's native dashboards
- You lack the resources to own data pipelines and prefer a simpler tool
- A unified data layer that joins LIMS, ERP, grant budgets, and inventory into one warehouse
- Real-time answers to cross-system questions like burn-per-grant and cost-per-program
- Dashboards that refresh automatically instead of waiting on a manual Excel stitch
- A single source of truth leadership can trust for board and funder decisions
- Connections to your existing ERP, accounting software, and inventory management software as data sources
- The data-engineering work underneath is the real cost, and it is less visible than the dashboard
- You own the pipelines, so source-system changes can break feeds and need maintenance
- If your data is already in one clean system, you may only need Tableau, not a custom data layer
- Garbage in, garbage out: a custom layer cannot fix dirty source data without a cleanup effort
The honest cost picture for San Diego
| Project scope | Typical cost | Timeline |
|---|---|---|
| Data layer plus dashboards over two or three sources | $45k to $75k | 2 to 4 months |
| Full warehouse with pipelines and real-time dashboards | $85k to $130k | 4 to 6 months |
| Pipeline build feeding existing Tableau or Power BI | $40k to $70k | 2 to 3 months |
Feature priorities for San Diego teams
What we build under business intelligence dashboards in San Diego
The engagements San Diego teams bring us most often: KPI dashboards, data warehouse, embedded analytics, business intelligence dashboards, BI development and data visualization.
Exactly what you get
A data layer that pulls experiment data from the LIMS, spend from the ERP, budgets from the grant system, and stock from inventory into one warehouse, so a single dashboard answers burn-per-grant against milestones in real time. The CFO walks into a board meeting with live numbers, the program lead sees cost-per-program without an export, and the Excel stitch-up that used to feed everything is retired. The chart you see is the easy part; the trustworthy join beneath it is the value.
How to choose a developer in San Diego
Ask how they would join your LIMS, ERP, and grant data, not how pretty the dashboard will be, because the plumbing is the project. They should ask about data quality and refresh frequency early. San Diego's research-driven buyers reward a team that treats the data pipeline and model as the real deliverable, documented so the next analyst can trust and extend it, over one that demos a polished chart on sample data.
Timeline: what happens, and when
- !They focus on the visuals, not the data joins. Ask how they pull and unify your sources
- !They have no ETL or warehouse experience. Ask to see a data pipeline they built
- !They ignore source data quality. Ask how they handle dirty inputs
- !They promise real-time without addressing refresh cost. Ask how often each source updates
- !No plan to maintain pipelines. Ask what happens when a source system changes
Most San Diego 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 can't Tableau answer our cross-system questions?
Because Tableau visualizes data it does not join. If your numbers live in a LIMS, an ERP, and a grant spreadsheet, the dashboard cannot combine them without a data layer underneath, which is exactly what a custom BI build provides.
How much do custom BI dashboards cost in San Diego?
A data layer plus dashboards over a few sources runs $45k to $75k. A full warehouse with pipelines and real-time dashboards reaches $85k to $130k. A pipeline build feeding your existing Tableau lands at $40k to $70k.
Do we have to replace Tableau or Power BI?
Often not. The value is usually the data pipeline and warehouse beneath the dashboard. You can keep Tableau or Power BI as the visualization layer and build the plumbing that finally lets it join your sources.
Can it answer burn-per-grant in real time?
Yes, once the data is joined. With LIMS, ERP, and grant budgets in one warehouse, burn-per-grant against milestones refreshes automatically instead of waiting on a monthly Excel stitch.
What if our source data is messy?
A custom layer can include cleanup and validation, but it cannot magically fix dirty inputs for free. Expect a data-quality effort as part of the build, because trustworthy dashboards require trustworthy sources.