Your Provo SaaS board asks for MRR and your direct-sales side asks for downline performance, and one Tableau can't answer both
Tableau, Power BI, and Looker visualize clean data beautifully, then a Provo company needs SaaS metrics (MRR, churn, NRR) and direct-sales downline performance in one place, computed from messy source systems, and the BI tool spends 80% of its effort on data modeling it was never meant to own. A custom BI layer with a real data pipeline runs $45,000 to $130,000 over 3 to 6 months, and the trigger is when your dashboards disagree because everyone defines the metric differently.
Your Provo company has Tableau, and it makes pretty charts. The problem is upstream: MRR is calculated one way in the board deck, another way in the sales dashboard, and a third way in the spreadsheet finance trusts. Looker assumes a clean modeled warehouse, but your data is scattered across your SaaS app, Stripe, QuickBooks, and a direct-sales system, and nobody has reconciled what a metric actually means.
Power BI can connect to all of it, but connecting is not modeling. Your SaaS metrics and your direct-sales downline performance live in different shapes, and stitching them into one trustworthy view is real data engineering, not a dashboard exercise. So leadership gets three numbers for the same question and trusts none of them, which is worse than having no dashboard at all.
- The same metric is calculated differently across dashboards
- Your data is scattered across app, Stripe, QuickBooks, and a sales system
- Leadership distrusts the numbers and falls back to spreadsheets
- SaaS and direct-sales metrics need to sit in one view
- Your data already lives in one clean warehouse
- Standard SaaS metrics from a packaged tool are enough
- You have a small, well-defined metric set
- You lack capacity to maintain a data pipeline
- Every metric defined once and computed the same way everywhere
- SaaS metrics and direct-sales downline performance in one trustworthy view
- A real data pipeline reconciling app, Stripe, QuickBooks, and sales data
- Board-ready dashboards leadership actually trusts and acts on
- Self-serve views so teams stop building conflicting spreadsheets
- The data pipeline, not the charts, is the cost and complexity
- Garbage source data produces garbage dashboards; you must clean upstream
- You own pipeline maintenance as source systems and schemas change
- If your data is already in a clean warehouse, Looker alone may suffice
Business Intelligence Dashboards pricing in Provo: the real numbers
| Project scope | Typical cost | Timeline |
|---|---|---|
| Data pipeline plus core dashboards | $45k to $75k | 3 to 4 months |
| Unified SaaS and direct-sales metrics layer | $75k to $105k | 4 to 5 months |
| Full BI platform with self-serve and governance | $100k to $130k | 5 to 6 months |
The features that matter for Provo
Provo business intelligence dashboards: the full scope
The engagements Provo teams bring us most often: real-time analytics, KPI dashboards, data warehouse, embedded analytics, business intelligence dashboards, BI development and data visualization.
Exactly what you get
A BI layer where the data pipeline does the real work: it pulls from your SaaS app, Stripe, QuickBooks, and direct-sales system, defines each metric once, and serves one trustworthy view of SaaS metrics and downline performance together. It reads revenue from your accounting software, relationships from your custom CRM (Customer Relationship Management), and delivery data from your project management software, so a Provo leadership team finally gets numbers it can act on.
How to choose a developer in Provo
Ask where most of the work goes, and make sure the answer is the data pipeline and metrics layer, not the charts. A strong team interrogates how you define MRR before drawing a single graph. One that leads with dashboard mockups is solving the wrong problem. Provo's Silicon Slopes data talent understands SaaS metrics; favor a team that can also model a direct-sales downline, because unifying both is where the value is.
From kickoff to launch: the schedule
- !They focus on chart design; ask how they build and own the data pipeline
- !No single metrics layer; ask how MRR gets one definition everywhere
- !They ignore source-data quality; ask how they catch broken feeds
- !No plan to unify SaaS and direct-sales data; ask how the shapes reconcile
- !They quote on dashboards alone; ask what the pipeline costs
Most Provo 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 dashboards disagree?
Because the same metric is defined differently across tools and computed from scattered source data. A Provo SaaS needs a single metrics layer that defines MRR, churn, and downline KPIs once, so every dashboard reads the same trustworthy number.
Isn't Tableau or Power BI enough?
They are excellent at visualization but assume clean, modeled data. Your data is scattered across your app, Stripe, QuickBooks, and a direct-sales system, so the missing piece is a data pipeline and metrics layer, which is where a custom build spends its effort.
Can it show SaaS and direct-sales metrics together?
Yes, and that is often the point. A custom pipeline reconciles the different shapes of SaaS and direct-sales data so MRR and downline performance sit in one view, which off-the-shelf BI cannot do without that engineering anyway.
What does a custom BI dashboard cost in Provo?
A data pipeline plus core dashboards runs roughly $45k to $75k. A full BI platform with a unified metrics layer, self-serve, and governance reaches $100k to $130k over five to six months.