FAIR Principles: a data governance foundation
No company I’ve ever worked for has had impeccably clean data. But AI requires us to clean up our ‘stuff’ if we want to leverage it to unlock insights or patterns in our own organizations.
Data is the new oil. Its value can’t be overstated.
With Boards, communities and states demanding accountability and compliance (think PII, PHI, FERPA and COPPA), FAIR is your blueprint for transparent, efficient, and trustworthy organizational data practices. We recommend you play FAIR at the core of your data governance and AI adoption strategy.
Modern data governance is facing incredible scrutiny thanks to evolving regulations and greater public expectations for transparency, privacy, and ethical use. And, applying FAIR (Findable, Accessible, Interoperable, and Reusable) principles is essential if you want to properly protect your (and your customer’s / community’s) data or overlay a large language model (LLM) on your organizational data.
FAIR doesn’t just strengthen data governance; it makes your data optimally ready for AI.
By ensuring data is Findable, Accessible, Interoperable, and Reusable, you make it easy for LLMs to discover, retrieve, connect, and reuse high-quality datasets without costly, manual data wrangling. This accelerates value extraction and innovation from LLMs, enabling more accurate, robust, and explainable results, while reducing risks of bias or compliance gaps.
In this way, FAIR is not just a governance standard—it’s the foundation for truly leveraging AI across your enterprise.
Amid this landscape, the FAIR principles provide the essential foundation for effective, future-proof data governance and organization-wide adoption of AI as we build our organizational minds (O’Minds).
The FAIR Principles
Findable: Ensure that datasets within your organization are clearly catalogued, richly described with metadata, and assigned unique, persistent identifiers. This makes data assets easy to locate, both for your team and external auditors or potential acquirers when requested. (Your data room will be ready - not waiting to be built!)
Accessible: Govern data so that, within appropriate privacy and security controls, employees and other stakeholders can retrieve it using standard procedures and protocols. Accessibility also means documenting data usage rights and maintaining audit trails of who accessed data, when, and why. (These audit trails should include prompts, responses, etc. by the way. Find out how we recommend you do so here.)
Interoperable: Store data in standardized, open formats, and use common nomenclature relevant to your industry, making it possible to combine, compare, and share data across departments or with trusted partners. This is crucial as organizational structures become more complex (e.g. humans working with agents) and data-sharing more regulated.
Reusable: Make sure every data asset has clear documentation, context, and licensing or usage guidelines. Include details about data quality, origin, and intended application so teams can confidently and legally reuse data across projects; minimizing risk and maximizing efficiency.
Why does FAIR matter for data governance?
The EU AI Act, the GPAI Code of Practice and our own Strategic AI Adoption Framework (SAiAF) emphasize record-keeping, traceability, and demonstrable accountability. A FAIR approach makes fulfilling these obligations part of your operating rhythm and workflows. FAIR polices can eliminate data silos, streamline compliance reviews, and boost team productivity.
Customers, partners, and regulators increasingly demand proof that you handle data responsibly. FAIR-based governance provides those assurances—and helps turn compliance into a competitive differentiator. When data is findable and accessible, less time is wasted searching or questioning its origin and quality.
Practical steps to make data governance FAIR
Adopting FAIR principles in your data governance program means embedding both technical and people-centered practices across your organization. Here’s how to translate FAIR from principle to reality, including the crucial role of training your teams for sustainable, effective data stewardship.
Findability
First of all, centralize your data inventories. Implement a searchable, continually updated data catalog where every dataset and resource is uniquely identified and described with clear metadata (e.g., owner / author, source, creation date). AI can help here once the template has been set.
Then assign persistent, unique IDs or (even better) Digital Object Identifiers (DOIs) to your datasets so they can be easily referenced in documentation, code, or audits.
And most importantly (for your teams to understand), define and enforce naming protocols and metadata requirements for all files and datasets, ensuring searchability and traceability by people as well as AI.
People & training focus
Train staff to create, name, document and catalog files consistently, emphasizing why detailed metadata is foundational for transparency and efficiency. And as you’re developing AI Literacy (which should always cover data as well as people governance). But don’t wait for that. Include exercises on data cataloging, navigating your organization’s data sets and metadata curation in your onboarding and refresher courses.
Accessibility
Use tiered access controls. Implement permission structures so that data is accessible to the right people but protected from unauthorized use. This is especially important when you’re dealing with Personally Identifiable Information (PII) or Personal Health Information (PHI). Make sure everyone understands what constitutes PII or PHI if you hold any. (By the way, HR usually understands these concepts very well. Ask your internal experts along with your IT resources!)
Implement standard retrieval protocols. Ensure data can be retrieved using documented, user-friendly procedures (APIs, self-service portals and LLMs). But make sure you train on the protocols in addition to documenting them.
Maintain logs of data access, including who accessed what data, when, and for what purpose. These audit trails will save you should you ever need to provide proof of data integrity.
People & training focus
Provide regular training on data access procedures and responsibilities for handling sensitive data. Build awareness campaigns on privacy, consent, and ethical use, linking day-to-day access to compliance requirements. Share, don’t scare.
Interoperability
Interoperability is something that most organizations don’t worry about from a general practice standpoint. But as we have more applications sharing data and the desire for LLMs to help us understand our own data, it’s increasingly important that everyone understands what it is and why we need it.
Leverage open formats and standards where possible. Store data using widely accepted, non-proprietary formats (e.g., CSV, JSON, XML). LLMs can help here, as long as they’re fully licensed (paid plans) and training is turned ‘off.’
Adopt shared data dictionaries, systems, and defined terminologies within and between teams. We emphasize shared, common language in our AI Literacy trainings, and it’s foundational to understanding how to manage data in addition to how to work together with AI.
Your development teams should design data systems to integrate easily with third-party tools, platforms, and regulatory monitoring solutions. Integration efficiency will serve you well. Standards are there for a reason. Follow them.
People & training focus
Train both technical and business users in the organization’s chosen formats and standards. Business users should be held to much higher data management standards than ever before, and they will, once they understand the why as well as the how. Coach teams on collaborating across departments using interoperable, shared schemas and data definitions. Practice and reap the rewards.
Reusability
Develop clear Licensing and Usage Guidelines. Define and communicate what each dataset can be used for—covering rights, restrictions, and citation requirements.
Document the history of all data sources, collection methods, validation routines, and any transformations applied. Again, we’ve developed a great process here for using LLMs to create files, etc. It can be followed for other data provenance (history-in-the-making).
Periodically perform quality checks and review data for accuracy, completeness, and relevance, and flag issues for remediation.
People & training focus
Regularly update staff on your organization’s data use and reuse policies, any regulatory confinements and the importance of proper attribution and compliance. And we recommend you embed regular practices such as data quality reviews and documentation sprints into team routines. This is likely a new organizational muscle - you’ll need to build it.
Making FAIR sustainable: the role of people and training
A truly FAIR data governance program succeeds only when staff at all levels—data creators, users, and stewards—understand their roles and responsibilities. Practical training should:
Start at every employee (or AI Agent) onboarding.
Be ongoing, tailored by department and data sensitivity.
Emphasize not just the “how,” but the “why” behind FAIR practices.
Use real-life scenarios (case studies, exercises) that connect compliance and business value.
Foster a culture where stewardship is recognized and rewarded, making FAIR the shared language of data across your organization.
Building FAIR into your governance is as much about people as it is about policies and platforms—combine all for a resilient, accountable, and future-proof approach to data.
Resources from AIGG on your AI journey
Is your organization ready to navigate the complexities of data governance, AI implementations and to build trust and mitigate risks with confidence?
At AIGG, we understand that adopting AI isn’t just about the technology—it’s about doing so responsibly, ethically, and with a focus on protecting privacy while building trust. We’ve been through business transformations before, and we’re here to guide you every step of the way.
Whether you’re an edtech organization, school district, government agency, other nonprofit or business, our team of C-level expert guides - including attorneys, anthropologists, data scientists, and business leaders - can help you craft programs and practices that align with your goals and values. We’ll also equip you with the knowledge and tools to build your team’s literacy, your responsible practices, TOS review playbooks, guidelines, and guardrails as you leverage AI in your products and services.
Don’t leave your AI journey to chance.
Connect with us today. This next-step is simple. Start your journey towards safe, strategic AI adoption and deployment with AIGG.
