Commercial Data: What You Can Publish and How to Prepare It

If you need to share a sales spreadsheet or market dataset publicly, start by deciding which data you are allowed to publish, then build a table readers can interpret without access to your internal systems.

For this workflow, commercial data means information generated through business activity or supplied for business use: sales records, product prices, inventory, market research, or licensed financial data. It is not automatically public, and buying access does not automatically grant permission to republish it.

The useful result is a reviewed dataset with a clear row structure, defined measures, source information, and no sensitive content—not a public copy of the original business workbook.

1. Check permission before preparing the export

Separate three questions:

Check	What to establish before publication
Reuse rights	Does the applicable licence or agreement permit public display, redistribution, and your intended commercial use?
Confidentiality	Has the organisation approved release of the figures, including any supplier or client information?
Privacy	Could the table identify a person or reveal information about them, alone or combined with other sources?

Licences differ substantially. The UK’s Open Government Licence v3.0 permits publication, distribution, adaptation, and commercial exploitation of information expressly covered by it, subject to attribution conditions. It excludes personal data and third-party rights the provider cannot license.

By contrast, Alpha Vantage’s terms grant personal, non-commercial use unless otherwise agreed in writing. Their definition of commercial use includes use on behalf of a company. Check your actual agreement rather than assuming that an API key or subscription authorises a public dataset page. These examples reflect the terms available on October 28, 2026.

For purchased data, check permission for both the underlying rows and any derived summaries you intend to release. If permission is unclear, keep the data out of the public upload until it is resolved.

2. Create a separate public table

Work from a new export containing only approved fields. Do not upload a customer-level workbook and rely on hiding columns, sheets, or rows.

Exclude names, email addresses, account identifiers, precise customer locations, payment details, credentials, and confidential pricing or contract terms. Aggregation can reduce disclosure risk, but it is not proof of anonymity. The UK ICO’s anonymisation guidance explains that removing direct identifiers is insufficient when records can be linked to people through other information.

For a sales summary, a practical row definition might be one month × one sales region. Review whether unusually small groups or distinctive transactions would still expose a customer or business relationship. Combine or omit those groups where needed, then reassess the resulting table before release; do not treat an arbitrary minimum row count as a guarantee of safety.

Here is a fictional example, suitable for practising the workflow—not evidence of actual commercial performance:

month	sales_region	units_sold	net_revenue_usd
2026-01	North	100	3000
2026-01	South	80	2400
2026-02	North	130	3900
2026-02	South	110	3300

For this example, define net revenue as sales after discounts and refunds, excluding sales tax and shipping charges. Define units sold as units remaining after returns. Use your source’s actual definitions in a real dataset.

3. Validate the file, not just the spreadsheet view

Before publishing:

• Check uniqueness: each month–region combination should appear once in this summary.
• Reconcile totals: the example contains 420 units and $12,600 in net revenue. Your export should match the approved source summary.
• Keep totals separate: do not mix monthly observations and grand-total rows that readers might sum together.
• Check types: store revenue and unit counts as numbers, not strings containing currency symbols or explanatory notes.
• Explain missing values: distinguish unavailable or suppressed figures from genuine zeros.
• Reopen the exported file: verify headers, row count, dates, and numeric values after export.

Use one header row and consistent units. If you need help choosing a sharing format, see CSV vs XLSX.

4. Publish with enough context to interpret it

Prepare accompanying text stating the dataset’s source, reporting period, row definition, currency, measure definitions, exclusions, reuse terms, and update date. Label fictional or synthetic examples prominently. For real extracts, record the extraction date separately from the period measured.

TablePage supports CSV, TSV, XLSX, and XLS uploads and generates a public dataset page with a filterable table. Upload only the reviewed public file—not the working source.

Inspect the resulting page before distributing its link. Check that all expected rows appear, numbers remain numeric, and filters return the expected groups. In this example, January should show two rows totalling 180 units and $5,400. Pair the link with the definitions and source notes so readers can understand what the figures cover—and what they leave out.