On this page
- Key Takeaways
- Pipeline Health Is an Input, and Finance Wants the Output
- The Best Data Quality Metric Is Who Found the Problem First
- Reconciliation Hours Are a Payroll Line Nobody Labels
- Freshness SLAs Matter Less Than Decision Latency
- Platform Spend Without a Denominator Is Just a Bigger Number
- Reuse Is Where Data Engineering Pays for Itself
- Report Five Numbers, and Let Finance Own Some of Them
- FAQ
Key Takeaways
- Engineering metrics rarely survive the trip to the CFO's desk. Uptime, test counts and sources connected describe the plumbing. Finance is asking about cost, risk and speed.
- Who finds the bad number first is the most honest data quality metric. In one 2023 survey, business stakeholders caught data issues first in 74% of cases, up from 47% a year earlier.
- Reconciliation hours are a payroll line nobody labels. Count the time your analysts spend arguing about whose number is right, and put a cost on it.
- Platform spend needs a denominator. Cost per data product tells you whether spend is growing with use or with waste. Total warehouse spend tells you neither.
- Reuse is where data engineering pays for itself. Every new AI or analytics use case that starts on an existing, trusted data product is a pipeline you didn't pay to build twice.
Here's what bothers me about most data engineering reports. They read like a maintenance log. Pipeline uptime, tests passing, rows loaded, sources connected. All true, all useful to the people running the pipelines, and almost none of it answers the question a CFO brings to the meeting: what are we getting for this spend, and what does it cost us when it breaks?
The cost of getting this wrong is not small. Gartner's data quality research puts the average cost of poor data quality at $12.9 million a year per organization. (That figure is from 2020 research, so treat it as a direction, not a budget line. I'd bet the real number has gone up since, because more decisions now run through data than they did then.)
And the stakes are rising. Gartner also predicts that through 2026, organizations will abandon 60% of AI projects that aren't supported by AI-ready data. That makes data engineering the thing that decides whether your AI budget produces anything.
So the data team is more important than ever, and it is still reporting in a language finance doesn't speak.
My rule of thumb is simple. Every data engineering metric that goes to finance should map to one of three things a CFO already tracks: cost, risk, or speed of decisions. If a metric doesn't map to one of those, it belongs on the engineering team's own dashboard. It can still be a great metric for the people running the pipelines.
Pipeline Health Is an Input, and Finance Wants the Output
Uptime matters. So do test coverage and freshness. A data team that doesn't track them is flying blind. But they are inputs. They tell you the machine is running. They don't tell you what the machine produced, what it cost, or what happened when it didn't run.
Think about it from the CFO's chair. "99% on-time loads" sounds great until the one late load was the one that fed the board pack. "212 tests passing" says nothing about whether anyone trusts the revenue number. The engineering metric and the business outcome can move in opposite directions, and the report won't show it.
Sort every metric into cost, risk or speed
Take your current data team report and try to translate each line. Here's how the common ones map.
| Engineering metric | What finance hears | Report this instead | Bucket |
|---|---|---|---|
| Pipeline uptime / on-time loads | "Things mostly work" | Escaped incidents: bad numbers that reached a decision-maker first | Risk |
| Tests passing | "The team is careful" | Reconciliation hours: time spent settling whose number is right | Cost |
| Freshness SLA | "The data is recent" | Decision latency: time from a business question to a trusted answer | Speed |
| Total warehouse spend | "The bill went up" | Cost per data product, and spend nobody uses | Cost |
| Sources connected | "The team was busy" | Time to first data for a new use case, and reuse per data product | Speed |
Five numbers. Each one answers a question finance already asks. The rest of this post is about how to measure them honestly.
The Best Data Quality Metric Is Who Found the Problem First
I'll say this flat: the most useful data quality metric is who noticed the problem first. If your data team catches it, it's maintenance. If the CFO catches it in a board deck, it's a credibility problem, and those are much more expensive.
The trend here is going the wrong way. In a 2023 Wakefield Research survey of 200 data professionals commissioned by Monte Carlo, 74% said business stakeholders identify data issues first "all or most of the time," up from 47% the year before. The same survey found data downtime nearly doubled year over year, average time to resolve an incident rose to 15 hours, and the average share of revenue affected by data quality issues rose to 31%, from 26%. (Monte Carlo sells data observability, so read it with the sponsor in mind. I believe the direction. I wouldn't lean hard on the decimals.)
That 31% figure is the one I'd put in front of a CFO. It measures how much of the business bad data touches, which makes it a risk exposure number, and finance already knows how to think about risk exposure.
Count the incidents that reached a decision-maker
Log every data incident, and tag one field: who found it. Data team, automated test, or someone outside the data team. Report the third group monthly as "escaped incidents." It's a simple count, it's hard to game, and a falling number is the clearest evidence that data quality work is paying off.
Reconciliation Hours Are a Payroll Line Nobody Labels
Every company with more than one system has the meeting. Sales says revenue is one number. Finance says it's another. Someone spends the afternoon reconciling the two in a spreadsheet, and the actual decision gets pushed to next week.
That time is real payroll. It just never shows up as a line item, because it's spread across analysts, FP&A, ops leads and whoever owns the CRM export.
Say four analysts each spend five hours a week reconciling numbers between systems (illustrative, but ask your own team and see how close it is). That's 20 hours a week, about half of a full-time role, spent producing agreement instead of insight. Multiply by a loaded hourly cost and you have a number finance will take seriously.
This is where good data engineering pays back in a way a CFO can see. When customer, revenue and product are defined once, in one modeled layer, the reconciliation meeting gets shorter. Then it stops happening.
Ask the people doing the reconciling
Don't estimate this from the top. Ask the analysts and finance team directly: how many hours last week went to figuring out which number was right? Do it once a quarter. The trend matters more than the precision.
Freshness SLAs Matter Less Than Decision Latency
A freshness SLA tells you the data landed on time. Decision latency tells you how long the business waited for an answer. Those are different numbers, and the second is the one that costs money.
Data can be fifteen minutes fresh and still take two weeks to answer a question, because the question needs a new join, a new definition, a ticket in a backlog, and three rounds of "is this right?" Finance feels that delay. A pricing decision waits. A forecast goes out on last month's assumptions. An add-on acquisition model gets built on a spreadsheet someone stitched together by hand.
Decision latency is the metric that captures how usable your data is. It's also where AI starts to matter. Teams that model their data cleanly can put an agent or a natural-language query layer on top of it and turn a two-week request into a same-day answer. Teams that haven't can't, no matter which model they buy.
Time one real question end to end
Pick a question finance asked last quarter. Write down when it was asked, when the data team started, and when the answer was trusted enough to act on. Do that for five questions. You now have a baseline, and it will probably be uncomfortable.
Platform Spend Without a Denominator Is Just a Bigger Number
Data platform bills grow. Warehouses, ingestion tools, orchestration, observability. The CFO sees the total go up every quarter and has no way to tell whether that's success or sprawl.
There's good reason to ask. Flexera's 2026 State of the Cloud report estimates that 29% of IaaS and PaaS cloud spend is wasted. That covers all cloud spend, not data platforms specifically, but anyone who has looked at a warehouse bill knows where some of it lives: tables nobody queries, dashboards nobody opens, and jobs that rebuild the same data every hour for a report that's read once a month.
Total spend going up is fine if usage and value go up with it. The only way to know is to give the spend a denominator.
Tag spend to the data product that caused it
Track cost per data product, and cost per active consumer of that product. Then list the products with spend but no consumers. That list is your easiest savings, and it's the same discipline good teams already apply to AI spend: attribute the cost to the workflow that drove it, and cut what nobody uses.
Reuse Is Where Data Engineering Pays for Itself
This is the metric I care about most, and the one almost nobody reports.
The first AI use case on a new data foundation is expensive. You map sources, build pipelines, clean entities, write tests. The second one should be cheaper. The fifth should be much cheaper, because it builds on data products that already exist. If every new use case starts from scratch, you're paying for the foundation over and over.
That's how we build at PressW. Our Data Engineering work starts from the data a priority use case needs and ships governed data products that the next use case can build on, so new work starts from what already exists. The point is to make each new use case cheaper than the last.
Track time to first data for every new use case
When a team proposes a new AI or analytics use case, start a clock. How long until they have trusted data to build on? Track that per use case, and track how many use cases each data product serves. If time to first data is falling and reuse is rising, your data engineering spend is compounding. If not, you're running a series of one-off projects with a shared bill.
Report Five Numbers, and Let Finance Own Some of Them
Here's the whole argument in one view.
Escaped incidents. Reconciliation hours. Decision latency. Cost per data product. Time to first data. Five numbers, each one tied to cost, risk or speed, each one trended over time.
Building the pipelines is the part I worry about least. The harder part is getting people across teams to agree. Two of these numbers (reconciliation hours and decision latency) can only be measured with finance's help, and I think that's a feature. When finance co-owns the metric, the data team stops defending its budget and starts working on the same problem as the CFO.
I'll be honest about what I haven't figured out. I don't have a clean way to put a dollar value on a data product before it's built, and I'm skeptical of anyone who says they do. The best I've found is to measure these five things from day one, so that by the second or third use case the value is visible in the trend instead of argued in a meeting.
If your data team's last report was mostly uptime and test counts, try rewriting it around these five lines and see what questions it raises. If you want a second set of eyes on it, or help building the foundation behind it, our Data Engineering team does this work every day. Book a Discovery Call.
FAQ
A CFO should track data engineering metrics that map to cost, risk and speed: escaped data incidents, hours spent reconciling numbers, decision latency, cost per data product, and time to first data for new use cases. Engineering metrics like uptime and test coverage still matter, but they belong on the data team's own dashboard.
An escaped data incident is a data quality problem that a business user found before the data team or an automated test caught it. It's a useful finance-facing metric because it measures how often bad data reaches decisions, which is the real risk exposure.
Start with what you can count: hours spent reconciling conflicting numbers, hours spent resolving incidents, and decisions delayed or reversed because of bad data. Multiply the hours by loaded labor cost. Gartner's research put the average cost at $12.9 million a year per organization, though that figure is from 2020.
Decision latency is the time from a business question being asked to a trusted answer being available to act on. It's different from data freshness, which only measures how recently data was loaded. Long decision latency usually points to missing data models, unclear definitions, or a request backlog.
Report a small set of metrics tied to cost, risk and speed, trend them monthly, and let finance co-own the ones it can measure, like reconciliation hours. Showing that each new use case gets cheaper and faster because it reuses existing data products is the clearest proof of value.
Monthly works for most companies. Escaped incidents and platform spend can be reported every month, while reconciliation hours and decision latency are easier to sample quarterly. The trend over several periods matters more than any single number.
