# The value of data governance for data-informed healthcare projects

Data governance is about having the people, policies, processes, standards and technologies in place to manage data. It can be considered at a project or organisational level, or as part of a wider data sharing ecosystem.

This playbook aims to increase access to healthcare data and we refer to the [Open Knowledge Foundation’s definition of data governance](https://blog.okfn.org/2019/02/20/open-data-governance-and-open-governance-interplay-or-disconnect/): ‘the interplay of rules, standards, tools, principles, processes and decisions that influence what data is opened up, how and by whom.’

Data-informed healthcare projects often rely on accessing, using and sharing data from a variety of third party actors. Assessing, building and demonstrating trust in data and trustworthiness of data practices across this ecosystem can be a challenge. Implementing a robust data governance framework is one of the mechanisms by which trust can be built and trustworthiness can be assessed and demonstrated.

Considering data governance through the lens of accessing, using and sharing data as widely as possible helps to ensure the required skills, policies and processes to manage data in trustworthy, ethical and equitable ways throughout its lifecycle are implemented. This also leads to positive outcomes and value creation across the health data ecosystem, while avoiding harmful impacts.

Specifically, ensuring good governance of data in healthcare projects will help to:

* create efficiencies by streamlining operational models and processes to collect, use and share data across an organisation or wider ecosystem 
* enable collaboration across the healthcare data ecosystem, from research to patient care, through trustworthy data access, use and sharing 
* build trust between health data partners and data subjects 
* ensure data-informed decision making, creating better healthcare outcomes and optimised health services throughout the patient journey 
* optimise business models, resulting in increased private and public revenue 
* enhance research and innovation in healthcare service delivery.

Conversely, without good data governance in place, projects and organisations are exposed to a number of risks, including:

* Legal risks, for example liabilities not being well understood or well managed, potentially leaving data stewards exposed to expensive compliance failures. 
* Financial risks, for example inefficiently managed data leading to higher costs and/or data not being available to users when required. 
* Reputational risks, for example poor security processes leaving important datasets vulnerable and hindering data sharing or breaking trust between data partners, and thwarting innovation efforts. 
* Operational risks, for example not having timely discussions and agreements about roles, responsibilities and processes for collecting, sharing and using data assets.


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