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Data Governance Playbook
  • Health data governance: a playbook for non-technical leaders
    • Why data governance is important in healthcare
    • Who is this playbook for?
    • How to use this playbook
    • Other related resources
  • Index
  • Play one: Implementing data governance in healthcare
    • The value of data governance for data-informed healthcare projects
    • How to implement a data governance framework for a healthcare organisation or project
      • 1. Data assets
      • 2. People
      • 3. Policies and processes
      • 4. Standards and technologies
    • Resources relating to this play
  • Play two: Understanding and mapping health data ecosystems
    • Data ecosystems in healthcare
    • Data governance and trustworthy data ecosystems
    • Mapping the data ecosystem
      • Use case 1: Mapping the ecosystem of a Covid-19 symptom tracker in the UK
      • Use case 2: Identifying current stakeholders to reduce snakebite mortality and morbidity in India
    • Resources related to this play
  • Play three: Roles and responsibilities in health data governance
    • Roles involved in health data governance
      • Senior data leader
      • Health system leader
      • Policy leader
      • Health project partner
      • Governmental body
      • Senior executive leader
    • How to enlist support from stakeholders
    • Resources relating to this play
  • Play four: Making data interoperable
    • What is interoperability and how is it relevant to healthcare?
    • Standards for data and interoperability
    • Existing standards for data
    • Data adaptors
    • When to use an adaptor
    • Resources relating to this play
  • Play five: Demonstrating the value of health data governance: case studies
    • Primary care data use: MedMij platform
    • Using research data: INSIGHT Health Data Research Hub
    • Using healthcare data for other purposes: Infectious Diseases Data Observatory
  • Play six: Emerging uses of data and technology in the health sector
    • Emerging uses of health data
    • Emerging technologies to support health data management
    • Resources relating to this play
  • Play seven: Assessing the legal, regulatory and policy context for sharing health data
    • Data protection laws and policies
    • Intellectual property
    • Other regulations and laws impacting use of health data
    • Socio-cultural norms
    • Resources relating to this play
  • Play eight: Managing risks when handling personal data
    • Managing personal data responsibly and ethically in healthcare projects
    • What is personal data?
    • Data protection regulations
    • Recognising personal data in healthcare projects
    • Impacts from use of healthcare data
    • Minimising risk - practical approaches
    • Appendix: Risks from personal data exposure and how harms can be mitigated
  • Play nine: How to set up successful data sharing partnerships
    • Understanding how data sharing occurs in the health sector
    • A step-by-step guide to setting up successful data sharing partnerships
      • Step 1. Understand the purpose of sharing data, and with whom
      • Step 2. Define the principles that will guide how data is shared
      • Step 3. Build and maintain relationships with your data sharing partners
    • Appendix: International frameworks for data sharing principles
    • Resources relating to this play
  • Play ten: Sharing health data: data agreements and technologies
    • Common types of data sharing agreements
    • How to choose the best method of sharing data
      • Step 1: Decide how widely you need or want to share data
      • Step 2: Decide on the type of agreement required for sharing data
      • Step 3. Consider how technology can facilitate data sharing and access
    • Appendix: Choosing technology to support data sharing and access
    • Resources relating to this play
  • Play eleven: Cross-border data sharing
    • What is cross-border data sharing?
    • Current trends and global discussions on cross-border data sharing
    • Overcoming challenges with cross-border data sharing
  • How to support trustworthy data sharing: Checklist
  • Slides to communicate the benefits of data governance to key health stakeholders
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  1. Play one: Implementing data governance in healthcare
  2. How to implement a data governance framework for a healthcare organisation or project

2. People

Previous1. Data assetsNext3. Policies and processes

Last updated 3 years ago

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It is essential to have people with clear roles and responsibilities, resources and skills to do the job, for example training on data protection and privacy to ensure personal data is handled appropriately in accordance with regulations and to avoid harmful impacts on the people the data is about.

Key roles and responsibilities for data within a project or organisation include:

  • Board – accountable for the strategic direction of the project/organisation, including how data supports this.

  • Director – ultimately accountable for the data in their part of the business, including resourcing, risk management, who has access and who it is shared with.

  • Chief Data Officer (CDO) or data lead – depending on the size of the organisation, one director may be the CDO. This role oversees the range of data-related functions that may include data management, ensuring data quality and creating a data strategy.

  • Data governance manager – coordinates the approach to embed data governance frameworks across projects or the organisation.

  • Data steward – the technical expert who manages data on a day-to-day basis.

For additional guidance on the roles and responsibilities of other external actors in the health data ecosystem, see the section of the playbook.

Helpful questions to ask:

  • Do you have an expert internally on data governance? If not, do you have someone who is the go-to person for data?

  • Do you have the resources (budget/time) to manage data as a valuable asset?

  • Do you have buy-in across your project or organisation to look after data as an asset?

Useful resources to ensure people have the required skills and knowledge include:

  • The ODI’s illustrates how technical data skills must be balanced with other skills, such as service design, data innovation and change leadership, to help ensure data projects are impactful and lead to the best social and economic outcomes for everyone.

  • The ODI’s is a tool for anyone who collects, shares or uses data to identify and manage ethical issues – at the start of a project that uses data, and throughout.

  • A podcast, ‘’, explores the impact of poor data stewardship on companies’ reputations.

Mapping out flows of data and value exchange between actors you engage with can help to identify and prioritise people and communities to engage. The ODI’s provides a logical approach to doing this, and a way to visualise interactions and to communicate this to stakeholders.

roles and responsibilities
Data Skills Framework
Data Ethics Canvas
Why poor data governance could be a director’s undoing
data ecosystem mapping methodology