Hierarchy of Needs for Effective Data Collaboration

Data moves at the speed of trust. Without trust, even the most advanced technologies and partnerships will struggle and flounder. Establishing an environment of trust is the essential foundation for effective data sharing and collaboration. 

Level 5 focuses on building and maintaining trust of data sources and data sharing partners.  Within an institution, a successful trusted environment starts with a culture and operational structure that enables all sources and partners to be confident in the integrity, security, and ethical use of shared information. This environment minimizes concerns about privacy, security, data misuse, and compliance with legal and regulatory requirements.  Having a common understanding of what it means to be a trusted source of information and how prepared an institution is to be a trusted source is fundamental to effective collaboration. 

In today’s landscape, clarity about the use of artificial intelligence (AI) presents new challenges and opportunities. Trust issues around AI—such as algorithmic bias, explainability, and data provenance—can further complicate data sharing. Organizations must be transparent about how AI is used and governed, both internally and with partners, to maintain confidence and mitigate concerns. 

Without trust, organizations and individuals hesitate to share data, undermining the quality and utility of available information. This reluctance can hinder innovation, slow process improvement, limit research, and negatively impact outcomes—particularly in sensitive areas such as patient care. 

Building and maintaining trust involves: 

• Establishing clear expectations for data privacy, security, and responsible stewardship 

• Reassuring participants that their data will be protected and used appropriately 

• Governing data sharing with consistent and transparent agreements and standards 

• Ensuring transparency in AI and data practices 

• Fostering a culture of accountability and ethical data use 

A strong trust framework enables open collaboration, reduces barriers to participation, and establishes a solid foundation for advanced data integration and technical interoperability.  Establishing trust as the foundation creates a safe environment where partners are willing to contribute, collaborate, innovate, and resolve issues —making higher levels of effective data collaboration possible and more efficient. 

In today’s data-driven world, collaboration is essential—but it is only as strong as the policies and systems that support it. Level 4: Ability to Share Data is all about removing the barriers that can stand in the way of secure, compliant data sharing within and between organizations. 

Sharing data and insights with partner organizations can magnify the impact of opportunities; but if your privacy policies, existing data-sharing agreements, or compliance frameworks are not aligned to support successful sharing, you could be heading for complications or significant delays. Misaligned policies can lead to legal challenges, data breaches, or even a breakdown of the trust that was established in the previous level — none of these possibilities are desirable outcomes.  Equally important is the “need for speed” in responding to internal and external requests for data that extend beyond your current data environment to enable timely data-driven decisions. 

It is crucial for organizations to take a meaningful look at their current privacy statements, data use agreements, compliance frameworks, and data partner agreements. Set a goal to identify any gaps or outdated policies that could make data sharing risky, inefficient, or even not possible. By tackling issues like conflicting data use terms or noncompliance with regulations, you are continuing to build a foundation of trust and accountability. 

To ensure successful data sharing, organizations should start by: 

• Assessing current legal and policy frameworks by auditing privacy statements, data use agreements (DUAs), data licensing agreements (DLAs) and primary data sharing agreements (DSAs) to ensure compliance with regulations like HIPAA, California Consumer Privacy Act/California Privacy Rights Act (CCPA/CPRA), and other relevant laws. 

• Identifying barriers such as conflicts in data ownership, restrictive licensing terms, and technical gaps like inconsistent data formats or missing encryption protocols. 

• Updating and revising privacy policies and DUAs to clearly define permitted data uses (including use of data with AI tools), security standards, and shared organizational goals. 

Successful data sharing is not the sole responsibility of one person, and it will require collaboration across multiple teams including: IT, Finance, Legal, Procurement, Policy and Advocacy, Strategy, and Business Operations. IT can address specific technical issues and information security. Legal ensures compliance with laws and regulations, while Finance evaluates the financial impact. Procurement can ensure appropriate contract language regarding security and privacy for vendors.  Policy and Advocacy can align data practices with organizational values. The Strategy Team can assess the alignment with strategy and prioritization of resources.  Business Operations can assess the organization’s ability to collect, inventory, and manage the necessary data. Together, these groups create a strong foundation for effective data sharing. 

By systematically addressing strategic, legal, operational, and technical barriers, organizations will not just enable efficient data flows—they will foster a more collaborative environment built on trust, transparency, and shared success both internally and with data sharing partners. 

Level 3 shifts the focus from aligning all the internal organizational policies, agreements, and systems to ensuring that everyone involved - both internally and across partnering organizations - truly understands and consistently applies them together. This stage establishes a shared knowledge base where all data stewards interpret key concepts, such as terms of use, privacy protections, security standards, and the responsible, transparent application of AI, in the same way. 

This common understanding builds on the internal groundwork laid in Levels 5 and 4, where organizations established strong internal data, policies, processes, systems, and readiness. Level 3 extends this foundation by fostering shared agreements and mutual understanding with external partners around key areas such as data privacy, security, AI governance, and efficient data request and resharing processes. These shared commitments enable more seamless, trustworthy collaboration and improve operational efficiency across the data sharing ecosystem. 

Achieving this requires more than aligned agreements; it demands ongoing, cross-organizational training that covers not only the mechanics of data sharing but also the ethical, legal, and practical implications of emerging technologies including AI. Regular learning sessions build a strong data culture that keeps teams current on best practices, regulatory obligations, and emerging risks. This continuous investment strengthens trust, reduces misunderstandings, supports professional development, and ultimately promotes secure, efficient data collaboration. 

Practical approaches for fostering common understanding include: 

• Developing and delivering regular training on data stewardship, sharing agreements, privacy and security practices, and responsible AI use, tailored for both internal staff and external partners. 

• Creating and maintaining centralized, shared resource hubs with toolkits, common taxonomies and languages, FAQs, and technology-specific guidance, e.g., AI, accessible to all stakeholders. 

• Hosting regular collaborative forums such as data roundtables or workshops, where internal teams and partners discuss challenges, share updates, and reinforce shared commitments. 

• Building and updating comprehensive data dictionaries that define key data elements and their contexts across systems, surveys, and data collections; making these resources available through shared metadata repositories for all involved organizations. 

• Including explicit clauses in data sharing agreements that prohibit the re-identification of de-identified data 

While establishing clear data sharing agreements is a foundational step at this level, it is the combination of ongoing training, accessible shared resources, and active collaboration both within and across organizations that cultivates a culture of reliable, secure, and effective data sharing. These practices help prevent misunderstandings, streamline data exchange processes, and enable organizations to leverage data efficiently for operational improvements and non-commercial purposes. 

The increasing integration of AI into data ecosystems has demonstrated why maintaining a shared understanding internally and with partners is more important than ever-ensuring innovation is grounded in trust, transparency, and responsible stewardship. 

Level 2 builds on the previous level by committing organizations to common data standards - agreeing on how to consistently collect, define, aggregate, and report key data elements such as race/ethnicity, language proficiency, SOGI (Sexual Orientation and Gender Identity), and others. Consistency in data collection and reporting is the vital foundation for trusted, effective data sharing as well as the ability to augment information across a continuum. Adopting established standards and clear data definitions, along with transparency about data sources (e.g., self-reported versus third-party), internal processes for transformation or augmentation, and reporting protocols, enhances the accuracy, security, and value of shared data. 

This level not only reduces errors and enables accurate comparisons but also strengthens information security and privacy protections, making collaboration more reliable and efficient, and enhancing the research quality across organizations. Standardized data practices support interoperability and enable meaningful research, analytics, and insights across the data ecosystem. 

To implement these standards effectively, organizations should: 

• Leverage existing community, governmental, and standards organization frameworks. 

• Develop and maintain internal capabilities to apply shared definitions, formats, and de-identification protocols consistently, with commitment to updating practices as needs evolve. 

• Strengthen governance by modernizing systems with access controls, audit trails, and clear data breach response plans. 

• Document and make transparent all procedures for data collection, data hygiene, reporting, and de-identification. 

• Support partners through implementation and maintenance of agreed upon standards through technical assistance, training, and regular communication. 

• Establish a defined process for introducing new data standards and enhancing existing ones to ensure they continue to meet evolving organizational needs.  

Recognizing the importance of de-identification standards, initiatives such as the Physician Data Initiative (PDI) or a MedBiq work group could be well positioned to develop and share best practices in this area. Sharing these standards through resource libraries, if not already available, could further support the community's ability to implement consistent and responsible data practices. 

As data ecosystems become increasingly complex with AI integration, this shared clarity in data standards ensures that innovation rests on a foundation of transparency, trust, and responsible stewardship. 

Effective data collaboration rests on a strong foundation of trust, clear agreements, common data standards, and shared understanding. These essential layers ensure that everyone is aligned on how data is collected, managed, defined and used responsibly. Only with these pieces in place should organizations look to achieve Level 1: Technical Interoperability, where systems reliably connect, integrate, and exchange data securely. 

Technical interoperability addresses implementing the technical standards—such as data formats, APIs, and security protocols—that enable seamless, flexible, and secure data flow across the data ecosystem. However, without the foundational trust and standards beneath it, even the best technical solutions can fail to deliver true interoperability.  No technology enables sharing of information that cannot or will not be shared based on organizational issues. 

To achieve technical interoperability, organizations should consider these key actions: 

• Ensure that your organization is ready to share information (Have the foundational levels been addressed?). 

• Implement industry recognized technical standards to ensure system compatibility. 

• Provide the necessary infrastructure to maintain, update, and secure systems and data to keep pace with evolving technologies and security needs. 

• Enforce strong access controls and authentication to protect sensitive data. 

• Automate integration and reporting to reduce errors and improve efficiency. 

• Regularly test interoperability through system checks and partner collaboration. 

• Establish agreed upon change management and communication protocols to protect shared interoperability requirements. 

Building on the foundation of earlier levels enables technical interoperability to become the final step in implementation for an environment of innovation, operational efficiency, and effective data use in today’s complex technology ecosystems.