Why not just build it yourself?
The seven-layer climb.
That single morning briefing everyone expects, it sits on top of a seven-layer climb in data engineering, and each layer hits a snag that's specific to how GCs actually operate.
Every layer comes with a GC-specific headache
1
Integrate sources
Project IDs don't line up from tool to tool. And in Procore alone, you're staring at 200+ API endpoints.
2
ETL pipeline
Vendors tweak schemas without notice, pipelines fail quietly, and you usually learn about it at the Friday WIP meeting.
3
Data warehouse
Construction doesn't hand you a standard schema. You have to invent one, and you'll spend months there before a single useful query even runs.
4
Clean the data
Cost codes wander, duplicates creep in, fields turn into nulls. Cleaning isn't a phase, it's the job, and it never really ends.
5
Define KPIs
Ask five people for CPI and you'll get five definitions. Without shared rules, dashboards don't settle debates, they start them.
6+7
Build & visualize
Org changes keep coming, a new PM, a new division, a new tag setup, and suddenly the report breaks again. Analysts end up babysitting dashboards instead of pulling out insights.
What it really costs
12–18 months. $500K+. And then the next vendor update knocks it over.
Teams that try to do this in-house build integrations, stand up a warehouse, ship dashboards, then watch one vendor change snap the whole stack overnight.
Most internally built BI systems don't fail because nobody cared. They fail because the setup is fragile by design.
The replacement, by the numbers
13X
ROI on the rebuild
6,000+
hours saved per year
$1M+
BI cost avoided per year
weeks
to replace a 5-year build
The heavy lifting is already done.
Skip the seven-layer climb. See the output on your data.