Funding the Fix: How to Build the Business Case for a HubSpot Data Cleanup
Published June 14, 2026
TL;DR: Data cleanup loses budget fights because its cost is invisible and its benefit is the absence of a problem, while the features it competes against demo well. The fix is to make the cost of the current state legible before asking for the spend: quantify the recurring waste in terms the budget holder already tracks (hours, decisions, spend at risk), tie each number to a defect you can show, and propose the remediation in funded phases with a checkpoint after each, not one large irreversible project. A business case that names the bleeding, attaches it to evidence, and de-risks the spend with phasing is one a CFO can approve. This article gives the structure.
Why cleanup loses the budget fight
Every quarter, data remediation competes against work that is easy to picture: a new tool, a new channel, a new feature. Those rivals win because their value is concrete and forward-looking, while cleanup’s value is the quiet disappearance of a problem nobody has fully priced. The person holding the budget cannot approve what they cannot see, and “our data is messy” is not a number. The work of the business case is to convert a vague unease into a figure the budget holder already manages, then attach that figure to something they can verify.
Step 1: Quantify the current state in the budget holder’s units
A CFO or a head of revenue does not think in duplicate rates or null percentages. They think in hours, decisions, and spend. Translate the data problem into those three, and keep every figure conservative and sourced from the actual portal rather than a benchmark.
- Wasted hours. The recurring analyst and operator time spent reconciling reports, manually fixing records, and re-running queries that should be trustworthy the first time. This is the most defensible number because it is straight labor cost.
- Decisions on wrong numbers. The times a forecast, a headcount plan, or a budget allocation was made on a figure that later proved wrong. You do not need to invent a cost here; naming two or three real incidents is more persuasive than a fabricated dollar amount.
- Spend at risk. The marketing or outbound budget aimed through targeting and segmentation that runs on mis-classified or duplicated records. A portion of that spend reaches the wrong audience, and that portion is estimable from your own data.
State each as a conservative estimate with the assumption shown, not as a precise claim. A range you can defend beats a point estimate you cannot. The independent research on this is consistent in direction even where specific figures vary: Gartner has estimated that poor data quality costs organizations an average of $12.9 million per year (Gartner, Data Quality). Use that as corroboration of the pattern, not as your own number; your own number must come from your portal.
Step 2: Attach every number to a defect you can show
A figure with no evidence is an opinion. The reason an audit precedes the business case is that the audit produces the exhibits: the specific duplicate company records, the property that is blank on a large share of contacts, the two workflows writing the same field, the lifecycle stage that means different things in different reports. When each cost line points to a defect the budget holder can see on screen, the case stops being a complaint and becomes a finding. This is also what separates a fundable case from a feeling: the data foundation audit is the evidence-gathering step the business case is built on.
Step 3: Propose the fix in funded phases, not one large project
The fastest way to lose an approval is to ask for one large, irreversible spend against an estimate. The safer structure, and the one a finance owner is far more likely to approve, is phased: a scoped first phase with a defined deliverable and a checkpoint, then a decision to fund the next phase based on what the first one proved. Phasing does three things at once. It caps the downside if the estimate was wrong. It produces an early, visible result that builds confidence for the rest. And it converts a single high-stakes yes-or-no into a series of smaller, evidence-backed decisions, which is how budget holders prefer to commit money to anything uncertain.
A reasonable shape is: phase one establishes the true state and fixes the highest-cost, lowest-risk defects (deduplication, the most-read blank fields); a checkpoint measures the change against the phase-one estimate; later phases tackle the structural work (association integrity, workflow consolidation, definition governance) only if the first phase validated the model. One category of cleanup that requires its own sequencing care is permanent deletion for data-privacy compliance: removing a contact under a GDPR right-to-erasure request is irreversible, and the activity timeline that was associated to that contact can be left without a parent if the deletion is not prepared correctly. The method for executing that safely is in the guide on GDPR-safe deletion without orphaning activity. Each phase is independently justifiable, so the spend can stop at any checkpoint without having wasted the prior investment.
Step 4: Name what the spend buys, in their terms
Close the case on the outcome the budget holder cares about, not the technical fix. The deliverable is not “deduplicated records”; it is “a pipeline number you can defend in a board meeting.” It is not “property completeness”; it is “marketing spend that reaches the audience you paid to reach.” It is not “workflow consolidation”; it is “automations no one is afraid to change.” Translate every technical result back into the decision or the dollar it protects, because that is the language in which budgets are approved. That same cost discipline decides which tools to fund at all, the build, buy, or skip framework for RevOps spend.
The discipline
The recurring failure is asking for cleanup budget by describing the cleanup. Describe the bleeding instead, in the budget holder’s own units, attach each number to a defect they can see, and de-risk the spend with phases and checkpoints. A data cleanup framed this way is not a cost center asking for money; it is a remediation with a measured return and a capped downside, which is exactly the kind of spend that gets approved. The number was always there. The business case just makes it visible before it asks.
If you need to fund a data cleanup but cannot yet put a defensible number on the current state, that is the first deliverable, not a blocker. Start with a Data Foundation Audit, which produces both the figure and the evidence a business case is built on.
Sources
- Gartner, Data Quality topic page (the durable home of Gartner’s data-quality research and the widely cited 2021 estimate that poor data quality costs organizations an average of $12.9M per year): https://www.gartner.com/en/data-analytics/topics/data-quality
- Internal anonymized RevOps remediation engagements (no client names, no portal identifiers); all figures stated as conservative, portal-sourced estimates, not benchmarks.