Building the Internal Business Case for Selling Company Data

A good internal business case for selling data shows a realistic revenue range, the full cost of preparing the data, the rights position, the risks with their mitigations, and a small, reversible first step. It answers the questions legal, security, finance and the executive sponsor will each ask, in that order of concern.

Selling data touches legal, security, product, finance and customer trust at once, so no single team can approve it alone. A business case lets each function see its own concerns addressed and gives the sponsor a clear decision to make instead of a vague idea to bless.

Most internal proposals fail for avoidable reasons.

They state an exciting revenue figure with no basis. They skip the rights question, which then surfaces in a legal review and kills momentum. Or they ask for a large commitment before any evidence exists. A business case that is honest about uncertainty and asks for a small first step is easier to approve than an ambitious one.

You do not need a long document. Four to six pages, or a short deck, is usually enough if each section does real work. The structure below is a working outline, not a formula.

What should the business case contain?

It should contain eight parts: the proposal, the data in scope, the rights position, the value estimate, the costs, the risks, the pilot plan and the decision requested. Each answers a question that someone in the review will otherwise raise later.

1. The proposal in two sentences: what you want to sell, to whom, and why now.
2. The data in scope: sources, volume, date range, format and what is deliberately excluded.
3. The rights position: who owns the data, what contracts and consents apply and what legal review is needed.
4. The value estimate: a range, with assumptions and a method.
5. The costs: preparation, legal, delivery, support and platform fees.
6. The risks and mitigations: privacy, confidentiality, competitive, reputational and operational.
7. The pilot plan: a narrow first step, a time box and success criteria.
8. The decision requested: exactly what approval you are asking for, from whom.

The sections below take the harder ones in turn.

How do you describe the data and prove you have the right to sell it?

Describe the data concretely, then show a clear ownership and rights position. Reviewers trust a proposal more when it states what is excluded and what is still unknown. A rights section that says "we are checking" with a plan beats one that says nothing.

Start from an inventory. How to run a company data inventory before you sell lays out the work: what datasets exist, where they live, who produced them and what they contain. For each candidate dataset, record the source, whether it includes personal information, whether customer contracts restrict onward use, and whether employees or vendors contributed content.

Then address ownership. Data your company generated about its own operations is a different case from data customers entered into your product, which is different again from employee communications. These categories are treated differently. Where a dataset contains customer content, your contracts and privacy notices decide what you may do, and some datasets will simply be off the table.

This is general information and not legal advice. The business case should name the lawyer or team who will confirm the rights position, and make that confirmation a gate, not an afterthought.

How do you estimate the revenue honestly?

Give a range, show the assumptions and say what evidence would narrow it. A single number invites challenge and gives you nothing to defend; a range with stated assumptions invites a useful conversation.

Build the range from at least two angles. First, a cost view: what would it take to recreate the data? Second, a market view: what do buyers pay for comparable rights, and what do early conversations suggest? Data valuation methods: cost, market and income describes each in detail, and the free ten-question valuation at /evaluate gives a quick starting estimate to put in the document.

Present three scenarios, labelled clearly as hypothetical until real offers arrive:

• Low: one buyer, a narrow evaluation or research license.
• Base: a small number of non-exclusive commercial licenses.
• High: a broader or exclusive deal, if demand and rights allow.

Do not include figures you cannot support, and do not borrow numbers from articles about other companies' deals. If you have no evidence yet, say so and make evidence-gathering the first goal of the pilot. A sponsor will respect a plan to learn cheaply more than a forecast that cannot be defended.

What costs belong in the business case?

Include every cost to prepare, sell, deliver and support the data, not only the cost of the license. Underestimating preparation work is the most common reason a data sale ends up less profitable than forecast.

• Data preparation: extraction, cleaning, deduplication, formatting and documentation.
• De-identification and review: removing personal and confidential information, and checking the result. How to de-identify company data before selling it describes the work and its limits.
• Legal: rights review, drafting or adapting license terms, and any privacy assessment.
• Security: access controls, secure delivery and review of what leaves the company.
• Staff time: engineering, legal, security, finance and the commercial lead.
• Sales and support: answering buyer questions, running samples, handling delivery issues.
• Platform fees: on LogitAtlas, listing is free and the platform commission is 10%, deducted when an order completes.
• Tax and accounting: advice on how proceeds are treated.

Treat internal staff time as a real cost even if no cash leaves the building, because it displaces other work. Reviewers will notice if you leave it out.

Which risks will reviewers raise, and how do you answer them?

Reviewers will raise privacy, confidentiality, competitive, reputational and operational risks. Answer each with a specific mitigation, and be willing to say that some risk cannot be removed and must be accepted or avoided by narrowing scope.

• Privacy: personal data in the dataset. Mitigation: exclude it or de-identify it, review the result and take legal advice on the applicable rules. Be honest that de-identification is hard and imperfect, and that some data is better left out.
• Confidentiality: customer or partner information embedded in documents or messages. Mitigation: contract review, scoping and redaction. Some material will be excluded outright.
• Competitive: a buyer builds something that competes with you. Mitigation: field-of-use limits in the license, and not selling core advantage data.
• Reputational: customers or employees object. Mitigation: transparency in your notices where appropriate, and a rule that you do not sell anything you would be uncomfortable explaining publicly.
• Operational: delivery failure, disputes or support burden. Mitigation: clear listing descriptions, tested delivery and escrow-backed payment.
• License risk: a buyer uses the data beyond what you intended. Mitigation: explicit permitted-use, term and deletion terms.

Where a risk is large, say whether the proposal accepts it, mitigates it or removes the dataset from scope. A proposal that has already cut its riskiest data is easier to approve.

How do you make the first step small enough to approve?

Propose a pilot with a narrow scope, a time box, a capped budget and written success criteria, so the decision is easy and the downside is limited. A reversible first step answers most objections.

A pilot might look like this, as a hypothetical outline:

1. Choose one dataset that clears the rights and privacy checks, preferably the cleanest and least sensitive one.
2. Prepare it and document it to a standard you would show a stranger.
3. List it with a clear description and a narrow evaluation or non-exclusive license.
4. Run for a fixed period, say a quarter, and track enquiries, bids and objections.
5. Review: what did buyers ask, what did they offer, what did preparation really cost?
6. Decide whether to expand, adjust or stop.

State success criteria in advance. They might include a minimum number of qualified buyer conversations, a bid in your target range, or a completed order with no dispute. Also state stop conditions, such as a rights problem discovered during review. When the plan has a clear exit, it is much easier for a sceptic to say yes.

How do you tailor the case to each stakeholder?

Lead with the concern each person is paid to hold. The same facts can be presented differently for legal, security, finance and the executive sponsor, and a one-page summary per audience helps.

• Legal wants the rights chain, the license draft and the limits on use. Show the exclusions and the gating review.
• Security wants the delivery mode, the access controls and what leaves the company. LogitAtlas offers files in secure platform storage, a seller-hosted link revealed after payment, or scoped API credentials, so you can choose the mode that suits your security posture.
• Finance wants the range, the costs, the timing of cash and the tax view. Escrow means payment follows confirmed delivery, or auto-releases after 72 hours if the buyer takes no action.
• Product and customer-facing teams want to know the effect on customers and on competitive position.
• The executive sponsor wants the decision, the downside and the learning. Give them a clear yes-or-no with a small commitment.

Anticipate one more voice: the sceptic who says the data should simply be used internally. Take that seriously. What is data monetization compares internal and external value, and your business case should say why the external route is worth doing for this dataset.

What does a good decision request look like?

A good decision request names the approval you need, the people who must give it, the budget cap, the time box and the conditions that stop the project. It turns a discussion into a decision.

For example, you might ask for approval to complete a rights review on one named dataset, spend a capped amount on preparation, and list the result under a non-exclusive evaluation license for one quarter, with a review at the end. Attach the open questions. Say what you will report back and when.

When approval arrives, move quickly while the momentum lasts. You can start a listing at /sell once the rights review and preparation are complete.

Frequently asked questions

Who needs to approve selling company data?

Usually several functions: legal for rights and privacy, security for what leaves the company, finance for revenue and tax treatment, and an executive sponsor for strategy. Product or customer teams may also need a say if customer trust is affected. The business case should name each approver and what they must confirm.

How much revenue should I forecast in the business case?

Give a range, not a single figure, and label the scenarios as hypothetical until real offers exist. Build it from a cost view and a market view, state the assumptions, and make gathering real buyer evidence the first goal of a pilot. Avoid borrowing numbers you cannot support.

What is the biggest reason these proposals get rejected?

Usually unresolved rights or privacy questions. Reviewers will not approve a sale if nobody can say who owns the data or whether personal information has been handled properly. Address the rights position early, name who will confirm it, and exclude any dataset you cannot clear.

Should the first sale be exclusive?

Generally no. A non-exclusive, narrowly scoped first license limits your commitment and preserves future options while you learn what buyers value. Exclusivity ties up the data and should carry a clear premium, so it is better considered after you have real market evidence.

What if leadership thinks selling data is too risky?

Take the concern seriously and narrow the proposal. Cut the riskiest data, limit the license scope, propose a small time-boxed pilot with stop conditions and name the lawyer who will review rights. A reversible first step with a clear exit is much easier to approve than a broad programme.