What are the best practices for sharing your organization's data with research partners?

1 Data governance

Data governance is the set of policies, processes, roles, and standards that define how data is collected, stored, accessed, used, and shared within and outside the organization. Data governance is essential for ensuring that the data is consistent, accurate, reliable, and trustworthy, as well as compliant with relevant laws and regulations. Before sharing data with research partners, you should establish a clear data governance framework that specifies who owns the data, who can access it, how it can be used, and what are the expected outcomes and benefits. You should also assign roles and responsibilities for data stewardship, data quality management, data security, and data auditing.

2 Data quality

Data quality refers to the degree to which the data meets the requirements and expectations of the intended users. Data quality is influenced by factors such as completeness, accuracy, timeliness, validity, consistency, and relevance. Poor data quality can undermine the credibility and validity of the research results, as well as waste time and resources. To ensure data quality, you should follow data quality standards and best practices, such as defining data quality dimensions and metrics, performing data quality assessments and validations, documenting data sources and lineage, and resolving data quality issues.

3 Data security

Data security refers to the measures and controls that protect the data from unauthorized access, use, disclosure, modification, or destruction. Data security is crucial for preventing data breaches, cyberattacks, identity theft, fraud, and legal penalties. To ensure data security, you should follow data security principles and best practices, such as encrypting data in transit and at rest, implementing access control and authentication mechanisms, using secure data transfer protocols and platforms, applying data masking and anonymization techniques, and monitoring and reporting data security incidents.

4 Data ethics

Data ethics refers to the moral and ethical principles and values that guide the collection, storage, access, use, and sharing of data. Data ethics is important for ensuring that the data is used in a responsible, fair, transparent, and respectful way, respecting the rights and interests of the data subjects, the data owners, and the data users. To ensure data ethics, you should follow data ethics frameworks and best practices, such as obtaining informed consent from the data subjects, respecting their privacy and preferences, ensuring data fairness and diversity, disclosing data sources and methods, and avoiding data misuse and harm.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?