Data Sharing Policy Demystified: Everything You Need To Know

In today’s digital age, data has become a valuable currency that fuels innovation, drives decision-making, and shapes industries across the globe. As a result, understanding data sharing policies has never been more critical. Whether you’re a business owner, a consumer, or simply someone navigating the digital landscape, this guide aims to demystify the complex world of data sharing policies. We’ll explore what data sharing entails, and why it matters, and provide you with the essential knowledge you need to navigate this ever-evolving aspect of our interconnected world. So, let’s dive in and unravel everything you need to know about data sharing policies.

Data Sharing Policy Unveiled: Do You Really Need One?

Table of Contents

What Is a Data Sharing Policy?

A data sharing policy refers to a set of guidelines and procedures that dictate how data should be accessed, used, stored, and shared within an organization or between different entities. It is a crucial component of data management and sharing in any research project. The purpose of a data sharing policy is to ensure transparency, accountability, and the responsible use of data. It outlines the steps that need to be followed when collecting, analyzing, storing, and disseminating data.

A well-defined data sharing policy addresses issues such as consent for data sharing, intellectual property rights, privacy protection measures, security protocols for data storage and transfer, and mechanisms for resolving disputes related to data ownership or usage. By establishing clear rules and guidelines for handling data throughout its lifecycle, a data sharing policy promotes efficient collaboration among researchers while safeguarding the integrity and confidentiality of sensitive information.

Types of Research

There are two categories of research:

Primary Research

Primary research, also known as original research or field research, involves the collection of data directly from sources. Researchers conduct primary research to gather firsthand information specific to their research objectives. Common methods for primary research include surveys, experiments, interviews, observations, and focus groups. This type of research allows researchers to generate new data and insights that are tailored to their research questions and goals.

Secondary Research

Secondary research, also called desk research or literature review, involves the analysis and interpretation of existing data and information that has been collected by others. Researchers use secondary sources such as books, articles, reports, and databases to access data, findings, and knowledge that have already been published or documented. Secondary research is valuable for reviewing existing literature, comparing studies, and drawing on pre-existing data to inform research projects. It doesn’t involve collecting new data but rather synthesizing and analyzing existing information.

When Is a Data Sharing Policy Required?

Here are common scenarios in which a data sharing policy may be required:

Legal Requirements

Data sharing policies may be mandated by local, national, or international laws and regulations. For instance, the European Union’s General Data Protection Regulation (GDPR) requires organizations to have clear data sharing policies in place when transferring personal data across borders.

A Data Sharing Policy Should Meet All the Legal Requirements

Third-Party Relationships

When an organization collaborates or contracts with third parties, such as vendors, partners, or service providers, a data sharing policy is often essential to outline the terms and conditions of data sharing and data protection responsibilities.

Privacy Concerns

If an organization collects and processes sensitive or personal information, it may need a data sharing policy to demonstrate its commitment to safeguarding that data and respecting individuals’ privacy rights.

Research and Academia

Educational institutions, research organizations, and universities often require data sharing policies to ensure the ethical and responsible sharing of research data among researchers, students, and external collaborators.

Internal Data Sharing

Even within an organization, data sharing policies can be crucial to govern how different departments or teams share and access data. This helps maintain data integrity, security, and compliance with internal policies.

How To Create or Set up a Data Sharing Policy

Creating or setting up a data sharing policy is a crucial process to ensure responsible data handling and compliance with legal and ethical standards.

Here are steps to help you create an effective data sharing policy:

Identify Stakeholders

Determine who within your organization will be involved in the creation and enforcement of the data sharing policy. This may include legal experts, IT professionals, data owners, and compliance officers.

Understand Data Types

Categorize the types of data your organization handles, such as personal data, financial data, intellectual property, or research data. Understanding the nature of your data is essential for policy development.

Legal and Regulatory Compliance

Research and understand the relevant data protection laws and regulations that apply to your organization, such as GDPR, HIPAA, or industry-specific standards. Ensure your policy aligns with these requirements.

Define Purpose and Scope

Clearly state the purpose of the data sharing policy and its scope. Specify what types of data are covered, who it applies to, and under what circumstances data sharing is allowed.

Data Classification

Develop a system for classifying data based on sensitivity and importance. This classification will help determine the level of protection and access controls needed for each type of data.

Access Controls

Define who can access and share data and under what conditions. Implement role-based access controls to ensure that only authorized personnel can access specific data.

Data Handling Procedures

Create guidelines for data collection, storage, transmission, and disposal. Specify encryption, authentication, and other security measures to protect data during these processes.

Consent and Permissions

If applicable, outline the process for obtaining consent from data subjects when sharing their personal information. Ensure that data sharing is done in accordance with individuals’ preferences and legal requirements.

Monitoring and Auditing

Establish procedures for monitoring data sharing activities and conducting regular audits to ensure compliance with the policy. Define consequences for non-compliance.

Training and Awareness

Provide training and awareness programs to educate employees and stakeholders about the data sharing policy. Ensure that everyone understands their roles and responsibilities in maintaining data security and privacy.

Review and Update

Regularly review and update the data sharing policy to adapt to changing regulations, technology advancements, and evolving organizational needs.


Communicate the policy to all relevant parties, including employees, contractors, and partners. Make it easily accessible and provide resources for addressing questions or concerns.


Define enforcement mechanisms and consequences for policy violations. Ensure that violations are addressed promptly and consistently.

Testing and Incident Response

Develop a plan for testing the policy’s effectiveness and responding to data breaches or incidents. Outline the steps to take in case of a security breach or unauthorized data sharing.

Documentation and Records

Maintain detailed records of data sharing activities, permissions, and incidents. These records will be essential for compliance and accountability.

Common Components of a Data Sharing Policy

Here are common components of a data sharing policy:

Purpose and Scope

  • Clearly define the data sharing plan, including the reasons for sharing data with external parties.
  • Specify the scope of the policy, outlining what types of data are covered and which external parties are authorized to access the data.

Data Classification

  • Categorize data based on sensitivity and confidentiality levels (e.g., public, internal, confidential, highly sensitive).
  • Define the criteria for classifying data into these categories.
  • Have a clear data dictionary.
  • Explain the different access controls and security measures for each data classification.

Data Access and Authorization

  • Specify who has the authority to grant access to data and the process for requesting and approving data access.
  • Outline the roles and responsibilities of individuals or teams responsible for managing data access.
  • Describe the authentication and authorization mechanisms used to ensure that only authorized individuals can access the data.

Data Sharing Procedures

  • Detail the procedures for sharing data with external parties, including the steps for requesting, approving, and executing data sharing agreements.
  • Define the legal and contractual requirements for data sharing, such as data sharing agreements, non-disclosure agreements, and any compliance obligations.

Data Privacy and Security

  • Emphasize the importance of data privacy and security throughout the data sharing process.
  • Outline security measures, encryption standards, and data protection protocols that must be in place when data is shared externally.
  • Provide guidance on data anonymization and de-identification techniques when necessary to protect sensitive information.

Data Management and Sharing Plan

Efficient data management and sharing practices are essential for ensuring the transparency, reproducibility, and integrity of scientific research, instilling confidence in both researchers and the wider community.

To achieve these goals, a data sharing policy should include the following components:

Clear Guidelines on Data Management

A well-defined data management plan should outline how data will be collected, organized, stored, and preserved throughout the research process. This includes specifying file formats, metadata standards, and naming conventions to ensure consistency and accessibility. For example, the National Institutes of Health (NIH) keeps a list of data repositories dedicated to sharing scientific data. These repositories serve as centralized platforms where researchers can deposit and access various types of research data, including genomic data, clinical trial data, and other scientific datasets.

Data Sharing Plans

A data sharing policy should address how researchers will share their data with others. It should specify when and where the data will be made available (e.g., upon publication or after an embargo period) as well as any restrictions or conditions that may apply.

Procedures for Handling Data Requests

The policy should establish a process for handling requests from other researchers or interested parties who wish to access the shared data. This may involve establishing criteria for granting access, ensuring appropriate confidentiality measures are in place if necessary, and outlining procedures for addressing disputes or conflicts regarding access.

The Five Safes Framework

The safes Framework provides a structured approach for ensuring the security and privacy of sensitive research data, allowing researchers to balance the need for data access with the protection of individual privacy. This framework is commonly used in data-sharing and research environments, particularly in contexts where sensitive or confidential data is involved.

The Five Safes framework consists of five key dimensions or “safes”:

Safe People

This dimension focuses on ensuring that only authorized individuals or organizations have access to the data. It involves processes such as user authentication, data access agreements, and access control mechanisms to restrict access to approved users.

Safe Projects

Safe Projects involve defining and controlling the purposes for which data can be used. Researchers must clearly specify their research objectives and justify the need for accessing specific data, and data custodians should assess whether these purposes align with legal and ethical requirements.

Safe Data

This aspect involves de-identifying or anonymizing the data to remove or minimize personally identifiable information (PII) while still preserving the utility of the data for research. Techniques like data aggregation, masking, and generalization may be employed to achieve this.

Safe Settings

Safe Settings refer to the physical or virtual environments in which data is accessed and processed. These settings should have appropriate security measures in place, such as firewalls, encryption, and network controls, to prevent unauthorized access and data breaches.

Safe Outputs

This dimension focuses on the outputs or results of data analysis. Researchers must ensure that the results they produce do not inadvertently disclose sensitive information. Data output controls, such as statistical disclosure control techniques, may be applied to mitigate risks.

Legal and Ethical Considerations in Data Sharing Policies

Legal and ethical considerations play a significant role in the formulation of data sharing policies, ensuring that the rights and privacy of individuals are protected while facilitating scientific collaboration and knowledge dissemination.

Here are some of the considerations:

Privacy Protection

Data sharing policies must prioritize the protection of individuals’ privacy. This involves ensuring that personally identifiable information (PII) is anonymized or de-identified before sharing and implementing robust security measures to prevent unauthorized access or breaches that could compromise sensitive data.

Informed Consent

Ethical data sharing requires obtaining informed consent from individuals whose data is being shared. Clear and transparent communication about how their data will be used and shared is essential to ensure individuals have a choice and understand the implications.

Informed Consent Gives You the Right to Make Decisions About Your Data

Data Ownership

Organizations must establish clear guidelines regarding data ownership. This includes defining who owns the data, whether it’s the individual, the organization, or a combination, and how this ownership affects data sharing.

Data Security

Legal obligations necessitate rigorous data security measures to safeguard against unauthorized access and breaches. Compliance with data protection laws such as GDPR or HIPAA is critical, with penalties for non-compliance.

Data Accuracy and Integrity

Ensuring the accuracy and integrity of shared data is both ethical and legally mandated. Organizations should establish mechanisms for data validation, correction, and rectification when errors are identified. In most cases, publicly funded research data is owned by the public and it should be accessed with fewer limitations.

Data Sharing Best Practices

Below are the data sharing best practices:

Data Governance

Establish a robust data governance framework to define ownership, access rights, and usage policies for responsible data sharing. Ensure that data is accurate, consistent, and complies with relevant regulations. Regularly audit and update data governance practices to adapt to evolving business needs and compliance requirements.

Data Security

Prioritize data security through encryption, access controls, and monitoring. Protect sensitive information from unauthorized access, breaches, and cyber threats. Implement data classification to identify the level of sensitivity and apply appropriate security measures accordingly.

Data Privacy

Respect data privacy laws and regulations, such as GDPR or CCPA, by obtaining explicit consent for data sharing when required. Anonymize or pseudonymize data when possible to minimize risks to individuals’ privacy.

Data Sharing Agreements

Develop clear and legally sound data sharing agreements (DSAs) with partners or third parties. DSAs should outline data usage, retention, and responsibilities of each party, reducing the risk of misunderstandings or disputes.

Data Quality Assurance

Regularly assess and improve data quality. Employ data validation, cleansing, and profiling techniques to ensure that shared data is accurate and reliable. This enhances the value of shared data for all stakeholders.

Data Sharing in Practice

Several compelling case studies demonstrate the practical impact of NIH genomic data sharing and data management initiatives in various domains. In the realm of clinical trials, the utilization of NIH data management protocols has streamlined the collection, storage, and analysis of patient data, significantly enhancing the efficiency and rigor of medical research. Additionally, through the NIH GDS program, researchers across the globe have accessed and leveraged large-scale genomic datasets to uncover insights into diseases, genetics, and personalized medicine, exemplifying the transformative potential of data sharing in advancing scientific discovery and healthcare.

Sharing Sensitive Data Such as Medical Might Require a Comprehensive Data Sharing Policy

Frequently Asked Questions

What Are the Potential Risks Associated With Data Sharing?

Potential risks associated with data sharing include unauthorized access and misuse of data, breach of privacy, identity theft, and exposure to cyber threats. These risks can lead to financial loss, reputational damage, legal issues, and compromised personal information. Also, some research endeavors, such as qualitative or mixed-methods investigations, can generate scientific data that prove difficult to anonymize or continue to carry privacy concerns even after anonymization attempts. This is primarily because these datasets may contain elements that enable deductions about the identity of research participants.

How Can Organizations Ensure That Sensitive Data Is Protected During the Data Sharing Process?

To ensure the protection of sensitive data during the data sharing process, organizations should first implement strong access controls and encryption mechanisms to restrict access to authorized users only. Additionally, data anonymization or pseudonymization techniques should be employed to de-identify sensitive information whenever possible. Lastly, organizations should conduct regular security audits and assessments to identify vulnerabilities and ensure compliance with data protection regulations, continuously monitoring and enhancing their data sharing practices to mitigate risks effectively.

Are There Any Specific Regulations or Laws That Govern Data Sharing Policies?

There are specific regulations and laws that govern data sharing policies, such as the General Data Protection Regulation (GDPR) in the European Union, the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Also, the International Committee of Medical Journal Editors (ICMJE) requirements set the standard for data sharing statements in research publications. These regulations aim to protect individuals’ privacy and ensure secure handling of sensitive data during sharing processes.

What Steps Can Be Taken To Ensure Transparency and Accountability in Data Sharing?

To ensure transparency and accountability in data sharing, organizations should start by clearly defining data sharing policies and procedures, making them easily accessible to all stakeholders. Secondly, they should maintain detailed records of data sharing activities, documenting who accessed the data, when, and for what purpose. Finally, regular audits and assessments of data sharing practices should be conducted, and reports made available to relevant parties to ensure compliance with established policies and regulations, fostering a culture of transparency and accountability throughout the data sharing process.


A well-crafted data sharing policy is the cornerstone of responsible and effective data sharing practices within any organization. It serves as a roadmap for handling sensitive information, ensuring transparency, accountability, and compliance with privacy regulations. By defining clear guidelines, consent mechanisms, security measures, and governance structures, organizations can foster trust among stakeholders and reap the benefits of data sharing while minimizing risks. Regular reviews and updates to the policy will help adapt to evolving data landscape, making it a dynamic tool for responsible data management and collaboration in the digital age.

Damien Mather Damien is a cybersecurity professional and online privacy advocate with a bachelor of Computer Science. He has been in the industry for 20+ years and has seen the space evolve far bigger than he ever thought. When he is not buried in his research or going through code, he is probably out Surfing or Camping and enjoying the great outdoors. 
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