Get a Demo

White Paper

The true cost of building your own data warehouse.

The dashboard is the easy part. The foundation is what you pay for – and keep paying for. Every figure on this page links to its independent source, and the white paper runs the full analysis: the layers, the costs nobody budgets, and the one capability you can’t build later.

The Build

$200K-$1M+1

the market price of the work – what firms that build warehouses for a living charge for a medium-to-advanced implementation. And that buys a general-purpose warehouse, before any community college logic.

3 – 12 months1

the professionals’ timeline for a general-purpose build. A first-time, in-house build – done alongside day jobs – runs longer.

The real cost is people

Building in-house doesn't avoid that bill - it moves it onto your payroll.

$135,9802

median U.S. pay for a database architect – the market rate for one person who can build this. (BLS, May 2024)

$400K+ / year2

a lean team of 2-3 at market rates, plus benefits and overhead – every year. Two years of that is the consultants’ quote, paid in salaries. (ZogoTech estimate from BLS wage data)

A bidding war colleges lose

The market pays nearly double what colleges do.

Database architect, U.S. median2

$135,980

IR technical staff, higher ed average3

$74,924

“Higher education salaries have always been lower than those in the private sector... IT managers have very limited ability to compete on salary.” - EDUCAUSE 2024 Top 104

And pay is the top reason higher ed employees look for other jobs. (CUPA-HR, 2025)5

And the builders leave

A homegrown warehouse outlives the person who understands it.

1.7 yrs6

average time a data scientist stays with one employer. (365 Data Science, 2021)

97%7

of data engineers report burnout; 79% have considered leaving the field entirely. (2021 survey of 600 data engineers)

Who's supposed to run it?

2.9 FTE8

the entire average IR/IE office at a public two-year college. (AIR, 2024)

4.0 → 3.78

average IR staffing across higher ed, 2018 to 2024 – the bench is shrinking, not growing.

The meter never stops

Every SIS change, catalog update, and state formula: re-engineering.

45%9

of data professionals’ time goes to preparing and cleaning data. (Anaconda State of Data Science)

50%7

of data engineers say too much of their time goes to finding and fixing data errors. (DataKitchen and data.world, 2021)

Still planning to build?

Even if everything goes right, the road is still the road.

Even if

Your builder stays

You haven't removed the risk - you've hired it. Every definition on campus now lives in one head, and every question waits in one queue.

Even if

The payroll math works

You're paying market rate for a first attempt at problems others have already solved a hundred times.

Even if

The first version ships

Version one is the starting line. The first number that doesn't match the state report resets campus trust to zero.

The biggest cost never appears in the budget.

Two years of your college's attention spent building instead of acting - the security work IT defers, the research IR exists to do. And by the time it's built, purpose-built platforms are two more years ahead.

And the students who needed answers this term have come and gone.

A third option

You don't have to choose between building and buying.

Colleges have joined ZogoTech planning to build their own - and used the platform as a running start instead: the foundation live in months, their team building on top of it, not underneath it.

No new hires. No two-year detour. Your people start where a build would have ended.

Get the white paper →