Key Takeaways
- Measure the partner against your alternative. Your baseline is what you would have done instead, usually hiring in-house or keeping the manual workarounds. Write it down before kickoff.
- One line of math is enough to start. ROI = (hours returned + value pulled forward + spend retired − total cost) ÷ total cost.
- Speed is the line people forget. The months a partner saves you on the first use case are often worth more than the hours the pipelines save.
- Your own team's time is a cost. Leave it out and every engagement looks better than it was.
- A modest year-one number is a good sign. If the worksheet says 10x in year one, someone padded the inputs.
Most data engineering partners get judged twice. Once in the sales process, on promises. And once at renewal, on gut feel. Neither of those is a measurement.
That gap matters more than it used to. In IBM's 2025 CEO Study, surveyed CEOs said only 25% of their AI initiatives delivered the expected ROI over the last few years. A lot of those initiatives sit on top of data work, so when they come up short, the data engineering spend is the next line someone wants to cut. If you can't show what your partner produced, you'll lose that argument even when the work was good.
So this is a quickstart. One worksheet formula, one worked example, and the three mistakes that most often skew it. It won't capture everything a good data foundation does for you (I'll say what it leaves out). It will give you a number you can defend in a budget meeting by the end of the week.
Measure Against the Alternative You Would Have Chosen
The most common mistake happens before anyone touches a spreadsheet. People compare the partner to zero, as if the data work would have cost nothing and taken no time without them.
That was never the choice. The real choice was one of three things: hire data engineers and build it yourself, keep running the business on exports and spreadsheets, or bring in a partner. Each has a cost and a timeline. The partner's ROI is the difference between the path you took and the path you would have taken.
The build-it-yourself path costs more than it looks, too. In a 2021 Wakefield Research survey commissioned by Fivetran, data and analytics leaders at companies with $100 million+ in revenue reported that their data engineers spent 44% of their time building and rebuilding pipelines, which the survey put at about $520,000 a year for a median team. (Fivetran sells pipeline automation, and the study is a few years old, so treat it as a direction. Still, anyone who has watched a team rebuild the same export three times will recognize it.)
Write your baseline down before kickoff
One paragraph is enough. Which path would you have taken without the partner? How long would the first use case have taken on that path? Which manual work would have continued in the meantime? If you write this after the engagement, you'll remember the alternative as faster and cheaper than it was. Everyone does.
The Whole Worksheet Fits on One Line
Here's the formula:
Partner ROI = (Hours returned + Value pulled forward + Spend retired − Total cost) ÷ Total cost
Four inputs. Each has a simple way to count it.
- Hours returned. Manual data work the new pipelines replaced: hand-pulled exports, copy-paste between systems, weekly reports someone assembles every Monday. Count hours saved per week × weeks × loaded hourly rate. Only count hours people now spend on something else.
- Value pulled forward. How many months sooner your first use case went live compared with your baseline, times what that use case is worth per month. This is the line where a partner earns most of its keep, because the in-house path usually starts with a hiring process.
- Spend retired. Tools, licenses and contractor-maintained scripts you switched off because the new stack covers them. Use the annual cost.
- Total cost. Partner fees plus your own team's hours on the engagement, at a loaded rate.
Measure the value lines over the 12 months after go-live. Keep it to year one. That keeps the number honest and comparable.
Count your team's hours as cost
Every engagement needs your people: a data owner who answers questions, someone in ops who knows where the spreadsheets live, a sponsor who unblocks access. Those hours are real. Put them in the denominator.
A Worked Example, With Deliberately Boring Numbers
Say a mid-market company brings in a partner for a 12-week data engineering engagement. The baseline it wrote down: hire one data engineer, which it expected to take a few months before the first pipeline shipped. Every figure below is illustrative. Swap in your own.
| Line | How it was counted | Year-one amount |
|---|---|---|
| Hours returned | Ops spent 20 hours a week pulling ERP and CRM exports and building a weekly report. Now 4. 16 hours × 48 weeks × $75 | $57,600 |
| Value pulled forward | First AI use case live 3 months sooner than the hiring path. 3 months × $20,000 a month of work it handles | $60,000 |
| Spend retired | Legacy ETL tool and contractor-maintained scripts switched off | $24,000 |
| Partner fees | 12-week engagement | −$100,000 |
| Team time | 2 people × 5 hours a week × 12 weeks × $100 | −$12,000 |
Total value is $141,600. Total cost is $112,000. Net is $29,600, so year-one ROI is $29,600 ÷ $112,000, about 26%.
That's it. Not 400%. Not 10x. A solid, positive return that a CFO will believe, because every input traces to something you can check.
And notice where it came from. Take away the $60,000 of pulled-forward value and the engagement loses money in year one. The speed was the return. That's worth knowing before you renew, and before you compare a partner's quote with a job posting.
Sanity-check the result before you share it
Run three quick checks. Would the ops team agree with the hours number if you asked them directly? Did the retired tools actually get canceled, or are they still on the card? Is the monthly value of the use case a number the business owner would sign? If any answer is "sort of," cut that line in half and rerun it. A smaller number you can defend beats a bigger one you can't.
Three Ways This Math Goes Wrong
These come up often enough to name.
- Counting the whole year of the use case as partner value. The partner gets credit for the months it saved you. The rest of the year, the use case would have been running on your baseline path too.
- Counting hours nobody redeployed. If the analyst who used to build the Monday report now spends that time in a different spreadsheet, the hours haven't come back. Count only the time that moved to other work.
- Counting reuse in year one. Good data work makes the next use case cheaper, and that's real. It's also the easiest line to inflate before it happens. Leave it out of year one and let it show up in year two.
Run It at 90 Days, Then Again at 12 Months
The first run is mostly a forecast. At 90 days you'll have the cost side nailed down and early evidence on hours. At 12 months you'll have real numbers on all four lines. Run both, and compare the second against the first. The gap tells you how good your estimates were, which makes the next business case easier to write.
Agree on the inputs in the statement of work
The cleanest version of this happens when the partner and the client agree on the four inputs before the work starts: which manual work is in scope, which use case goes first, which tools should be retired. Then nobody argues about the method at renewal. You only argue about the results, which is the conversation you want.
That's how we'd want to be measured at PressW. Our Data Engineering work starts from the data a priority use case needs and builds pipelines, models and data quality checks around it, so the first use case ships on a foundation the next one can use. If you're not sure which use case should go first, an AI & Data Readiness Assessment is a good place to settle that before any pipeline work begins.
A Rough Number Beats a Confident Story
I'll be honest about the limits. This worksheet misses things. It doesn't price risk, like the board deck that didn't go out with a bad number in it. It doesn't price the decisions you made faster, and it ignores reuse on purpose. I don't have a clean way to put those in a year-one formula, and I'm wary of anyone who claims to.
But a rough number you built from your own inputs is worth more than a polished story from a vendor deck, including ours. Start with the one line. Write down your baseline. Fill in the four inputs with numbers your team will agree with.
If you run it and the answer surprises you, in either direction, I'd like to hear about it. And if you want help building a data foundation that holds up to this kind of math, Book a Discovery Call.
FAQ
Compare the partner against the path you would have taken without them, usually hiring in-house or keeping manual workarounds. Add up hours returned, value pulled forward by shipping sooner, and spend retired, subtract total cost, and divide by total cost. Measure over the 12 months after go-live.
A modest positive number is realistic. Most of the value of data engineering compounds as later use cases reuse the same foundation, so year one tends to understate it. A year-one ROI of several hundred percent usually means an input was inflated.
Yes. Your data owners, ops leads and sponsors spend real hours on the engagement. Multiply those hours by a loaded hourly rate and add them to partner fees as total cost.
A partner brings a team that can start right away, while hiring usually means months of recruiting before the first pipeline ships. That time difference is often the largest line in a data engineering partner ROI calculation, which is why the worksheet counts it as value pulled forward.
Run the worksheet twice: at 90 days, when costs are known and early hours data is in, and at 12 months, when all four inputs are measurable. Comparing the two shows how accurate your original estimates were.
