"What did the data platform actually deliver this year?" A VP of Engineering asked me that in a budget review, expecting an answer like "$4.2M in attributable value." What he got from the team in the room was uptime percentage and a dashboard adoption count. Both are real numbers. Neither answers the question. I watched the platform's budget get cut 20% the following quarter — not because the work wasn't valuable, but because nobody in that room could connect the spend to a number the VP could defend to his boss. That gap between "we know this is valuable" and "we can prove this is valuable in dollars" is the single biggest threat to a data platform's funding, and it has nothing to do with the technology.
This is the measurement side of data strategy that platform teams chronically underinvest in: how to value data as an asset, the real difference between monetizing data directly and capturing its value indirectly, why showback and chargeback are the accountability mechanism that makes any of this credible, and the attribution trap that turns most "data ROI" slides into numbers nobody actually believes.
Is data really an asset, and can you put a number on it?
Infonomics — a term coined by analyst Doug Laney — is the discipline of treating information as a genuine economic asset and applying formal valuation methods to it, the way a company values inventory or intellectual property. The pitch is straightforward: if data drives decisions and revenue, it belongs on the same kind of ledger as the things a CFO already tracks, not in a separate "IT spend" bucket that only ever shows up as a cost. Three valuation approaches do most of the work in practice, and they answer different questions, so picking the wrong one for the audience is a common, avoidable mistake.
| Approach | Question it answers | Typical use |
|---|---|---|
| Cost-based | What would it cost to recreate or replace this data? | Insurance, disaster-recovery justification, "why this is worth protecting" |
| Market-based | What would a third party pay for this data? | Data products sold externally, licensing, M&A due diligence |
| Economic / utility-based | How much measurable business outcome does this data drive? | Internal ROI cases — the one that matters for most platform teams |
For a platform team trying to justify its own budget, the economic/utility approach is almost always the right one, and it's also the hardest, because it requires tracing a causal line from a dataset or pipeline to an actual business outcome — which is exactly the attribution problem this article spends the most time on. Cost-based valuation is useful for a narrower argument ("this dataset took 18 months and $2M to build, here's why losing it would hurt") but it doesn't tell anyone whether the data is actually worth what it cost.
What's the difference between monetizing data directly and capturing its value indirectly?
Direct monetization means data (or a product built on it) generates revenue on its own — selling a data feed, licensing an aggregated dataset, charging for an API built on proprietary data, or running a marketplace listing. It's the cleanest case to make to a board because the number lands straight on a P&L, no inference required. It's also the smaller opportunity for most organizations — building a sellable data product is a real product effort with its own quality, support, and legal bar (anonymization, licensing terms, usage auditing), not a side effect of having a data warehouse.
Indirect value capture is where almost all of a typical data platform's actual value lives, and it breaks into three categories worth separating because they're measured differently:
- Cost avoidance: a churn model that flags at-risk accounts before they leave, fraud detection that blocks losses before they happen, a forecasting model that prevents overstock. The dollar amount is a counterfactual — "what didn't happen" — which is inherently harder to defend than a counted transaction.
- Decision quality: better, faster decisions because the right number was available at the right time — a pricing decision informed by real margin data instead of a guess, an inventory call made same-day instead of next-week. This is the hardest category to put a number on and the easiest to overclaim.
- Risk reduction: compliance posture, audit readiness, reduced breach exposure from better governance. Often valued as "cost of the bad outcome we didn't have," which is the same counterfactual problem as cost avoidance, one layer removed.
Lead with direct monetization in board conversations when you have it — it's the only category that doesn't require anyone to trust your counterfactual math. But don't let its rarity make you dismiss indirect value as "soft." A fraud model that blocked $3M in losses last quarter is real money even though no invoice says so; the job is building a credible, agreed-upon way to count it before the board meeting, not after someone challenges the number live.
How do showback and chargeback turn cost into an accountability system?
Showback reports what each team's data and compute consumption actually costs, without moving money between budgets — it's visibility without consequence, and it supports what the FinOps community calls the "inform" phase: a shared, trusted view everyone agrees on before anything gets enforced. Chargeback goes further and actually allocates that cost to each team's or product's budget, with real financial consequences for usage — it supports the "optimize" phase, because now a team that runs an inefficient pipeline feels it in their own numbers, not the platform team's.
This matters for ROI measurement specifically because it's the mechanism that makes the cost side of any ROI calculation credible. A common, hybrid pattern — also standard in cloud FinOps generally — is chargeback for the 70-80% of spend that's clearly attributable to a specific team or workload, and showback for the remaining shared infrastructure (the platform team's own compute, a shared orchestration layer, central governance tooling) that doesn't cleanly belong to one consumer. Without this allocation layer, "ROI" calculations end up comparing a specific, attributed benefit against a vague, unattributed total platform cost — which is how a genuinely valuable use case gets blamed for the cost of ten mediocre ones running on the same shared cluster.
graph TD
INV["Data platform investment
(infrastructure + people)"]
COST["Showback / chargeback
(cost allocated by team/product)"]
USE["Usage: pipelines, models,
dashboards, data products"]
DIRECT["Direct monetization
(sold data, licensed feeds)"]
INDIRECT["Indirect value
(cost avoidance, decision quality,
risk reduction)"]
REPORT["Value realized vs cost allocated
(reported per team, per product)"]
INV --> COST
COST --> USE
USE --> DIRECT --> REPORT
USE --> INDIRECT --> REPORT
The value chain that makes a data ROI number defensible end to end. Cost has to be allocated down to the team or product level (showback/chargeback) before it can be fairly compared against the value that same team or product generated — comparing attributed benefit against unattributed total cost is the single most common error in data ROI reporting.
Why are most "data ROI" numbers vanity metrics?
Because they measure activity instead of outcome, and activity is what's easy to count. "500 dashboard views," "12 models in production," "99.9% pipeline uptime" are all real, all easy to pull from a system, and all answer a different question than "what did this change in the business." I've sat through board decks built entirely on activity metrics, and the tell is always the same: nobody in the room can answer "so what happened because of this" without a long pause.
The deeper problem is attribution — proving that a specific data investment caused a specific business outcome, rather than merely coinciding with it. A churn model gets deployed and churn drops the same quarter the sales team also launched a new retention campaign: which one gets the credit? The honest answer requires either a controlled experiment (a holdout group that didn't get the model's recommendations, so you can measure the actual delta) or, where that's not feasible, an explicit, agreed-upon attribution methodology decided before the result comes in — not reverse-engineered afterward to justify a number leadership already wants to hear. Teams that skip this step end up with a number that collapses under the first skeptical question, which is worse for credibility than not presenting a number at all.
The fastest way to lose a board's trust on data ROI is presenting an indirect-value number nobody can defend under questioning — and it usually only takes one bad quarter to do it. I've seen a team claim full credit for a revenue lift that a separate marketing campaign mostly drove, get publicly corrected by the CFO's office months later, and lose credibility for every subsequent number from that team for over a year — including the genuinely solid ones. Build attribution methodology and get stakeholder buy-in on it before presenting a number, use holdout groups or A/B comparisons wherever the use case allows it, and when a number is genuinely uncertain, present a range with the methodology shown rather than a single confident figure you can't defend live.
What does a practical measurement framework actually look like?
The teams that do this well don't try to value everything from day one — they build the measurement framework in the same incremental sequence as the platform itself, which echoes the sequencing argument in data strategy more broadly: prove value early and specifically, then generalize the framework once it's trusted.
- Tier 1 — efficiency metrics: the easiest to instrument and the least persuasive on their own — pipeline reliability, time-to-data, cost per query. Necessary as a baseline (you can't credibly claim value improved if reliability is unknown), insufficient as the headline number.
- Tier 2 — decision and process metrics: time saved in a specific workflow, faster decision cycles, reduction in manual reconciliation work. Tie these to a named team and a named process, not a platform-wide average — specificity is what survives a skeptical question.
- Tier 3 — monetized outcomes: direct revenue, quantified cost avoidance with an agreed attribution method, or risk reduction priced against a real incident-cost baseline. This is the tier that goes in the board deck, and it should only contain numbers that have already survived a Tier 2-level scrutiny internally.
# A value-tracking definition, kept next to the metrics layer
# (see also: a metrics layer for getting one agreed definition of revenue)
# rather than recreated ad hoc for each board deck
metric: fraud_model_value_q2_2026
tier: 3_monetized_outcome
type: cost_avoidance
method: holdout_comparison
holdout_pct: 10
baseline_loss_rate: 0.034
treatment_loss_rate: 0.019
estimated_value_usd: 2840000
confidence: medium
owner: risk-data-team
reviewed_by: finance_partner
last_validated: 2026-06-15Notice the owner and reviewed_by fields in that definition — the methodology being signed off by a finance partner, not just the data team, is what makes a Tier 3 number survive contact with a board. A number the data team alone vouches for is a data team opinion; a number finance has reviewed and agrees with the methodology behind is closer to fact. This is also where DORA-style delivery metrics connect to the value story: a platform with a fast, reliable lead time for shipping new data products is the platform that can capture monetizable value sooner after an opportunity appears, which is itself a quantifiable input to the ROI case, not just an engineering vanity metric.
What to carry away
Data ROI fails to land with leadership for a specific, fixable reason: most teams report activity (uptime, dashboard views, models shipped) when the room is asking for outcome (dollars, risk avoided, decisions improved). Infonomics gives you the vocabulary — cost-based, market-based, and economic/utility-based valuation answer different questions, and the economic/utility approach is the one that actually justifies a platform budget. Direct monetization is the easiest case to make because it needs no attribution argument; indirect value (cost avoidance, decision quality, risk reduction) is where most of the real value lives and where the discipline of honest measurement matters most.
None of it is credible without showback or chargeback underneath it — you can't claim ROI without an agreed, defensible cost basis to compare against. And the single biggest risk isn't measuring too little, it's presenting a number that collapses under one skeptical question because the attribution wasn't decided in advance or signed off by finance. Build the measurement framework in tiers, get the Tier 3 numbers reviewed before they reach a board deck, and remember that a smaller number you can defend beats a bigger number you can't.