Make data accessible by helping the right people find, understand, and use it for legitimate work—not by giving everyone unrestricted access. A finance team can make routine, low-risk information easy to use while protecting personal, sensitive, classified, or rights-restricted data through proportionate permissions, clear ownership, and regular review.
What accessible data means in practice
Accessibility has three parts: people can discover that a dataset exists, understand what it means, and obtain the level of access their work requires. A dataset that is hidden, poorly described, or available only through an unclear request process is not very usable, even if it is technically stored in a shared system.
Access should match the risk and the task. For example, a broad group might need summary budget information, while access to identifiable payroll or customer records may need tighter limits. The goal is not maximum openness or maximum restriction; it is useful access with safeguards suited to the data.
How to make data accessible without compromising security
Build accessibility into governance in a deliberate sequence. The UK Cabinet Office’s Data Sharing Governance Framework, published in 2022 for UK government departments and agencies, offers a practical example: it emphasizes accountable leadership, appropriate access, findability, standards, and monitoring. It is not a universal legal rule or a framework for every organization.
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1. Set an organizational mandate and name owners
Leaders should state what responsible data sharing is meant to enable, who is accountable for decisions, and how teams raise difficult or high-risk requests. Publish a policy or plan that explains how data is classified, shared, protected, and reviewed. Give each important dataset a responsible owner who can answer questions about its meaning, permitted use, and access decisions.
Ownership matters because technical administrators can configure permissions, but they may not know whether a proposed use is appropriate. The UK framework assigns ultimate accountability to the relevant senior official and calls for ownership across leadership; organizations elsewhere can adapt the accountability pattern to their own structure and rules.
2. Inventory and classify what you hold
Create a catalogue of datasets and record, at minimum, their owner, purpose, description, format, sensitivity, access route, and applicable restrictions. Note whether data includes personal or sensitive information, a security classification, intellectual-property constraints, or other limits on sharing. Review what is already exposed publicly, including old copies and endpoints that may no longer be needed.
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Classification should guide handling rather than become a reason to hide everything. Low-risk, non-personal information can often be made easier to find and use. Personal, sensitive, classified, or rights-restricted material needs an appropriate assessment before sharing.
3. Make approved data findable and interpretable
For data that is suitable to share, provide a catalogue entry or clear landing page with definitions, date or update information where relevant, quality notes, formats, terms of use, and a contact for questions or access requests. Document available APIs and their limits. Use shared naming conventions and definitions so that two teams do not mistake differently defined measures for the same thing.
Discovery need not mean public release. A catalogue can show that a restricted dataset exists, describe how it may be requested, and identify the owner without exposing the underlying records. If a full dataset is not appropriate for a user, a limited API or a smaller approved extract may meet the need with less exposure.
4. Match permissions to roles, purpose, and context
Start with least privilege: provide only the access needed for a defined job, and avoid broad standing permissions when narrower ones will work. Role- or group-based permissions suit stable job responsibilities and are often straightforward to administer. But role alone may be too broad when a person’s purpose, the requested action, or the situation changes the risk. In those cases, purpose-based rules or attribute-based access control (ABAC) can add relevant conditions.
| Control approach | What it considers | Useful when | Trade-off |
|---|---|---|---|
| Role- or group-based access | A person’s assigned job role or group membership | Responsibilities are stable and the group’s data needs are well defined | Easy to understand and administer, but a role can be broader than a particular task requires |
| Purpose-based access | Why the data is being accessed, as well as the user’s authorization | A legitimate use must be distinguished from other uses by the same role | Can express the intended use more clearly, but requires a way to define and govern permitted purposes |
| Attribute-based access control (ABAC) | Relevant attributes of the user, data, requested action, or environment | Permissions depend on context or need finer-grained conditions than roles provide | Can represent more nuanced rules, but depends on accurate attributes and disciplined rule governance |
This is a practical comparison, not a measured ranking of control models. AWS, Microsoft, GOV.UK, and NHS England guidance each support elements such as least privilege, role or group controls, contextual or purpose-based controls, and separation of duties; the appropriate mix depends on the organization’s data and operating environment.
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Where relevant, separate incompatible duties so one person cannot both perform and approve a sensitive action. Keep permissions auditable, and make clear who can approve exceptions. The control design should account for data sensitivity, the stability of job roles, purpose, context, administrative effort, auditability, and the friction users face.
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5. Offer safer alternatives to full access
When unrestricted access to a complete dataset is not justified, consider a less exposing way to support the work:
- Provide only the fields or records required for the task.
- Use an API with defined limits instead of distributing a full copy.
- Consider pseudonymisation for person-level data where the task does not require direct identifiers; pseudonymised data still needs appropriate protection.
- Use a controlled environment for work that requires more sensitive data than can safely be copied or broadly shared.
- Give users a clear request route and a contact who can consider a narrower alternative if the requested access is too broad.
Who should have access to company data?
People should have access when it is authorized, appropriate to their work, and no broader than necessary. A job title alone is not enough to settle every decision: the dataset’s sensitivity, the user’s purpose, the requested action, and relevant context can all matter. The data owner should be identifiable, while the organization should define who approves routine access, exceptions, and higher-risk sharing.
For financial information, this may mean making approved summaries available to teams that need them while limiting access to detailed personal or commercially restricted records. The exact boundary depends on the organization’s duties and the applicable privacy, records, security, and sector rules.
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Monitor access and keep governance current
Access decisions are not permanent. Log and audit access in a way that supports accountability, review shared resources and entitlements, and remove permissions when a person changes roles or no longer needs them. Review public data and exposed services as well as internal permissions. Track where users encounter delays, unclear definitions, or repeated exceptions so governance can address the cause rather than accumulating one-off workarounds.
Set a recurring review process with named owners, a route for escalation, and documented actions. The review frequency should reflect the sensitivity and rate of change of the data; the cited guidance does not establish one universal schedule for every organization.
Adapt the framework to your jurisdiction and sector
The UK Cabinet Office framework applies to UK government departments and agencies, excludes councils, and notes devolved-administration arrangements. GOV.UK’s principles and NHS England’s information-governance framework also address government or health-service contexts. They are useful governance examples, not substitutes for rules that apply to a private company, another country, or a different sector.
Before sharing data, map the applicable privacy, records-management, security, contractual, intellectual-property, and sector-specific requirements for your organization. WHO’s data principles support openness subject to legitimate justification and applicable policies, but do not create a universal access mandate. When rules or risk are unclear, escalate to the appropriate privacy, security, legal, or records specialist rather than treating openness or restriction as an automatic default.
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