The Real Cost of ‘We’ll Figure Out the Data Later’

A lot of teams say some version of this with a straight face:

We know the data is messy. We just do not have time to fix it right now.

That sounds pragmatic.

Usually it is expensive.

The cost of bad data work is easy to imagine when a migration goes sideways or a dashboard breaks in front of the board. The cost of delaying the work is harder to see, because it leaks out through slower decisions, repeated debates, and a growing pile of workarounds that everybody quietly starts treating as normal.

That is what makes “we’ll figure out the data later” so dangerous. The bill shows up in pieces.

The Four Costs Most Teams Underestimate

The damage usually shows up in four places.

1. Decision latency

When teams cannot get to one trusted number quickly, decisions slow down.

Budget reallocations wait. Pipeline calls turn into data debates. Roadmap decisions get pushed because nobody is confident in the signal. Forecast conversations get wrapped in caveats before anyone says the number out loud.

A lot of leaders treat this as a communication problem. It is usually a data trust problem.

If the CMO, CFO, and CRO need three follow-up meetings to align on what happened last month, that is not harmless process. That is time lost at the exact layer of the business where speed matters most.

Salesforce’s State of Data and Analytics (2026) found that data and analytics leaders estimate 26% of their organization’s data is untrustworthy and that only 49% of business leaders say they can reliably generate timely insights. That is the shape of decision latency in the real world: leaders are surrounded by data, but still cannot move quickly with confidence.

This is why conflicting metrics are not just annoying. They create operational drag. If that pattern feels familiar, Why Your CEO, CFO, and CRO Keep Getting Different Revenue Numbers is the sharper diagnosis.

2. Opportunity cost

Most companies do not miss growth because they lacked ideas. They miss it because they could not prove which idea deserved action.

So the team waits. Or spreads budget across five mediocre bets. Or keeps funding the channel that tells the best story instead of the one creating revenue.

The cost is not just wasted spend. It is the initiative you did not fund, the workflow you did not operationalize, or the pricing or product bet you delayed because the evidence never felt solid enough.

That hesitation is not imaginary busywork either. Slack’s Workforce Index research (2024) found that desk workers spend 41% of their time on low-value, repetitive work. In organizations with weak reporting trust, a meaningful slice of that effort gets burned on reconciliation, re-explanation, and spreadsheet patchwork instead of actual growth work.

This is the hidden tax of weak analytics maturity: the company becomes more cautious than it realizes, not because leadership is timid, but because nobody trusts the signal enough to move decisively.

3. The Trust Tax

This is the cost I see most often and the one teams almost never estimate.

The Trust Tax is the recurring time senior people spend reconciling reports, explaining caveats, or translating mismatched definitions instead of making decisions.

It looks like:

• a VP of Marketing rebuilding numbers in a spreadsheet before every exec meeting
• a RevOps lead explaining why Salesforce and finance do not match again
• a head of data getting pulled into ad hoc metric arbitration instead of shipping actual improvements
• leadership meetings where the first 20 minutes disappear into “which number are we using?”

None of that work shows up on a roadmap. But it absolutely shows up in payroll, morale, and decision quality.

Gartner notes that poor data quality costs organizations an average of $12.9 million per year. Even if your company is much smaller than the enterprise average behind that estimate, the direction is still useful: the financial damage from weak data compounds long before it becomes a visible crisis project.

And once a company gets used to it, people start budgeting for distrust as if it were normal.

4. Compounding debt

Every month you delay cleanup, the eventual fix gets harder.

New tools get added. Definitions drift further. Teams build local workarounds. Undocumented logic becomes culturally entrenched. A spreadsheet starts as a patch and ends up as a mission-critical system with no owner.

This is why data debt behaves more like interest than like a one-time repair bill. The problem does not wait politely for a better quarter. It compounds.

A Simple Trust Tax Calculator

You do not need a perfect model to estimate the cost. You just need an honest one.

Start with these four questions:

1. How many senior people regularly spend time reconciling or defending numbers?
2. How many hours per week does that actually consume?
3. What is a reasonable loaded hourly cost for those people?
4. How often does weak data delay or weaken a material decision?

A simple baseline formula is:

Trust Tax = people involved × hours per week × loaded hourly cost × 4.3 weeks per month

Example:

• 4 leaders involved
• 3 hours per week each
• $150 loaded hourly cost

That is 4 × 3 × 150 × 4.3 = $7,740 per month.

That number still excludes missed opportunities, delayed launches, and bad allocations. It is just the recurring cost of organizational friction.

A Back-of-Napkin Worksheet

If you want a quick internal estimate, walk through this:

Step 1: Estimate reconciliation time

• Weekly exec or leadership meeting time spent debating numbers: ____ hours
• Analyst, RevOps, or data-team prep time spent cleaning or re-explaining metrics: ____ hours
• Manager time spent building one-off spreadsheets or side analyses: ____ hours

Total hours per week: ____

Step 2: Estimate who is involved

List the recurring participants:

• Leadership: ____ people × ____ hourly cost
• Functional operators: ____ people × ____ hourly cost
• Data / RevOps team: ____ people × ____ hourly cost

Step 3: Estimate decision slippage

Ask:

• How many decisions this quarter were delayed because the data was not trusted?
• How many bets stayed alive longer than they should have because nobody could prove they were weak?
• How many high-value ideas were deferred because confidence in the inputs was too low?

Even directional answers are useful. The point is not accounting precision. The point is seeing the pattern clearly enough to act.

Where Teams Usually Misdiagnose the Problem

The common response is to ask for a new dashboard.

Sometimes that helps. A lot of the time it just puts cleaner design on top of unresolved trust problems.

If the issue is really ambiguous business asks getting translated badly into analytics work, read The Business Didn’t Ask for a Dashboard. They Asked for a Decision. If the issue is leadership alignment around revenue definitions, start with the metric conflict itself. If the issue is broader foundation debt, the answer is not another reporting layer. It is better source reliability, tighter models, shared definitions, and clear ownership.

What To Do in the Next 30 Days

If this article sounds uncomfortably familiar, do not start with a six-month modernization fantasy. Start smaller.

1. Pick the one metric conflict or reporting gap that is creating the most expensive friction right now.
2. Map the systems, owners, and definitions behind it.
3. Estimate the Trust Tax honestly.
4. Kill the worst workaround you are currently depending on.
5. Decide whether the next step is a focused diagnostic or a broader foundation fix.

The important thing is to stop treating the cost of waiting as zero. It is not zero. It is just distributed.

Bottom Line

“We’ll figure out the data later” sounds like a neutral delay. In practice, it means slower decisions, more political meetings, weaker bets, and a larger future repair bill.

The companies that get leverage from data are usually not the ones with the prettiest dashboards. They are the ones that stop paying the Trust Tax long enough to make decisions with confidence.

If leadership is already feeling that tax, Three Teams, Three Numbers is the fastest way to make the conflict visible. If the problem is structural, Data Foundation is the path to fixing what keeps recreating it.

Sources

• Salesforce, State of Data and Analytics (2026) — 26% of data estimated untrustworthy; 49% of business leaders say they can reliably generate timely insights
• Slack, New Slack research shows accelerating AI use and quantifies the “work of work” (2024) — desk workers spend 41% of time on low-value, repetitive work
• Gartner, Data Quality: Best Practices for Accurate Insights — poor data quality costs organizations an average of $12.9 million per year