Data protection regulations across the world are designed to minimise the risk of harmful impacts, while enabling personal data to be processed, that is, to be collected, accessed, used and shared. These regulations typically outline three key things:
Some types of personal data are more sensitive than others. Best-practice data-protection legislation defines sensitive personal information as ‘special category’ data and includes attributes such as race, ethnic origin, religious or philosophical beliefs, biometric data (where this is used for identification purposes) and health data.
Personal data: name, address, telephone number, IP address, location data, online identifiers (cookies)
Special category (sensitive) personal data: age, gender, race, religion or belief, political affiliation, biometrics, disability, criminal record, health, sexual orientation, relationship status
Personal data: name, address, telephone number, IP address, location data, online identifiers (cookies)
Special category (sensitive) personal data: age, gender, race, religion or belief, political affiliation, biometrics, disability, criminal record, health, sexual orientation, relationship status
To help manage this, and share data as widely as possible, there are several common mitigation options available to minimise the risks of re-identification:
Data protection regulations across the world are designed to minimise the risk of harmful impacts, while enabling personal data to be processed, that is, to be collected, accessed, used and shared. These regulations typically outline three key things:
Some types of personal data are more sensitive than others. Best-practice data-protection legislation defines sensitive personal information as \u2018special category\u2019 data and includes attributes such as race, ethnic origin, religious or philosophical beliefs, biometric data (where this is used for identification purposes) and health data.
Personal data: name, address, telephone number, IP address, location data, online identifiers (cookies)
Special category (sensitive) personal data: age, gender, race, religion or belief, political affiliation, biometrics, disability, criminal record, health, sexual orientation, relationship status
Personal data: name, address, telephone number, IP address, location data, online identifiers (cookies)
Special category (sensitive) personal data: age, gender, race, religion or belief, political affiliation, biometrics, disability, criminal record, health, sexual orientation, relationship status
To help manage this, and share data as widely as possible, there are several common mitigation options available to minimise the risks of re-identification:
Different countries will have specific laws and definitions but generally speaking, by default, the data creator holds exclusive rights to use the data, so that others must seek or be given the permission to use the data themselves.
Therefore it is important to review the terms under which you are using and sharing the data, to ensure you have the relevant permissions. These permissions are usually found in a licence accompanying the data, or in the contract (for example a data sharing agreement) setting out the terms under which data was provided.
" - } - ], - "examples": [ - { - "title": "Examples of data typically sourced from third parties:", - "text": "Different countries will have specific laws and definitions but generally speaking, by default, the data creator holds exclusive rights to use the data, so that others must seek or be given the permission to use the data themselves.
Therefore it is important to review the terms under which you are using and sharing the data, to ensure you have the relevant permissions. These permissions are usually found in a licence accompanying the data, or in the contract (for example a data sharing agreement) setting out the terms under which data was provided.
" + }, + "examples": { + "title": "Examples of data typically sourced from third parties:", + "text": "There may be other legal or regulatory considerations from non-data-related legislation, or specific to your sector (for example the Equality Act 2010 and freedom of information requests) that will need consideration when sharing data.
These considerations might require you to make the data available, or restrict you from doing so. There may also be requirements to remove or grant access to data after a set period of time.
This might include sector-specific legislation (for example financial institutions have particular duties and biometric data has particular limits).
It might also include data licensing or intellectual property laws; or insights into data rights, for example, individual rights to data, rights for data creators, rights for governments and rights for citizens.
" - } - ], - "examples": [ - { - "title": "Examples of other legal or regulatory considerations: ", - "text": "Local laws on competition, intellectual property, digital economy, human rights, equalities act.
Financial sector climate-related disclosures, oil and gas sector requirements around geophysical/seismic data, requests for environmental information.
National data sharing and access policies or frameworks, requirements from international organisations that promote a specific type of data access, and sector or country codes of practice.
" - } - ], - "mitigating_actions": [ - { - "title": "Mitigating actions", - "text": "If there are other relevant legal or regulatory requirements you may still be able to share the data. To manage any risks, you could carry out the below processes:There may be other legal or regulatory considerations from non-data-related legislation, or specific to your sector (for example the Equality Act 2010 and freedom of information requests) that will need consideration when sharing data.
These considerations might require you to make the data available, or restrict you from doing so. There may also be requirements to remove or grant access to data after a set period of time.
This might include sector-specific legislation (for example financial institutions have particular duties and biometric data has particular limits).
It might also include data licensing or intellectual property laws; or insights into data rights, for example, individual rights to data, rights for data creators, rights for governments and rights for citizens.
" + }, + "examples": { + "title": "Examples of other legal or regulatory considerations: ", + "text": "Local laws on competition, intellectual property, digital economy, human rights, equalities act.
Financial sector climate-related disclosures, oil and gas sector requirements around geophysical/seismic data, requests for environmental information.
National data sharing and access policies or frameworks, requirements from international organisations that promote a specific type of data access, and sector or country codes of practice.
" + }, + "mitigating_actions": { + "title": "Mitigating actions", + "text": "If there are other relevant legal or regulatory requirements you may still be able to share the data. To manage any risks, you could carry out the below processes:National security, is broadly defined as the safety of a nation against threats such as terrorism, war, natural disaster, and could be put at risk through the release of data. This includes any data that could be used to cause actual harm, deprivation or fear of the same.
If the data asset includes details that you think may impact national security, you may want to consider whether the data is already publicly available. It may be that the elements of the data you are concerned about are already shared by the government, public or private sector organisation. For example, transport infrastructure is broadly available and used by many organisations for route finding. If this is the case, then sharing the same data within your dataset is unlikely to cause increased risk.
" - } - ], - "examples": [ - { - "title": "Examples of data that could impact national security: ", - "text": "National security, is broadly defined as the safety of a nation against threats such as terrorism, war, natural disaster, and could be put at risk through the release of data. This includes any data that could be used to cause actual harm, deprivation or fear of the same.
If the data asset includes details that you think may impact national security, you may want to consider whether the data is already publicly available. It may be that the elements of the data you are concerned about are already shared by the government, public or private sector organisation. For example, transport infrastructure is broadly available and used by many organisations for route finding. If this is the case, then sharing the same data within your dataset is unlikely to cause increased risk.
" + }, + "examples": { + "title": "Examples of data that could impact national security: ", + "text": "Thinking about the ethical use of data is particularly relevant when insights drawn or decisions informed by data have the potential to directly or indirectly impact people and communities. When considering broader harmful impacts, think about the people the data is about, people impacted by its use, and the organisations using the data. For example, could use of this data result in decisions that discriminate against any groups or individuals, or impact their safety?
Bias can be conscious or unconscious and can result in under-representation of specific communities, which could impact them by giving an unfair advantage to others, or unfairly restricting access (for example, exclusive arrangements), therefore it is important to consider how data collection or use might be impacted by social or personal influences, and the potential consequential effects of this when that data is shared.
If the dataset is designed to benefit individuals and communities, have you considered the expected benefits for those that have directly or indirectly contributed to the creation of this dataset? Has this been documented? Have you communicated these benefits to them? Communicating the expected benefits for individuals or communities that have contributed to the creation of a dataset is essential to demonstrate transparency, build trust, and ensure ethical data practices. It acknowledges the value of their participation, informs them of potential positive outcomes, and promotes a sense of ownership and collaboration in data-sharing initiatives.
Data justice refers to the ethical and equitable use of data, emphasising fairness, inclusivity and transparency in data collection, processing and sharing. It seeks to address issues of discrimination, bias, and power imbalances in the context of data-driven decision making, ensuring that data-related processes benefit all individuals and communities equitably.
The ODI recommends the Data Ethics Canvas for examining the ethical aspects, and the Consequence Scanning and Risk Evaluation tool for looking at the expected outcomes from data sharing.
" - } - ], - "examples": [ - { - "title": "Examples", - "text": "To ensure ethical data sharing, consider the following actions:
Thinking about the ethical use of data is particularly relevant when insights drawn or decisions informed by data have the potential to directly or indirectly impact people and communities. When considering broader harmful impacts, think about the people the data is about, people impacted by its use, and the organisations using the data. For example, could use of this data result in decisions that discriminate against any groups or individuals, or impact their safety?
Bias can be conscious or unconscious and can result in under-representation of specific communities, which could impact them by giving an unfair advantage to others, or unfairly restricting access (for example, exclusive arrangements), therefore it is important to consider how data collection or use might be impacted by social or personal influences, and the potential consequential effects of this when that data is shared.
If the dataset is designed to benefit individuals and communities, have you considered the expected benefits for those that have directly or indirectly contributed to the creation of this dataset? Has this been documented? Have you communicated these benefits to them? Communicating the expected benefits for individuals or communities that have contributed to the creation of a dataset is essential to demonstrate transparency, build trust, and ensure ethical data practices. It acknowledges the value of their participation, informs them of potential positive outcomes, and promotes a sense of ownership and collaboration in data-sharing initiatives.
Data justice refers to the ethical and equitable use of data, emphasising fairness, inclusivity and transparency in data collection, processing and sharing. It seeks to address issues of discrimination, bias, and power imbalances in the context of data-driven decision making, ensuring that data-related processes benefit all individuals and communities equitably.
The ODI recommends the Data Ethics Canvas for examining the ethical aspects, and the Consequence Scanning and Risk Evaluation tool for looking at the expected outcomes from data sharing.
" + }, + "examples": { + "title": "Examples", + "text": "To ensure ethical data sharing, consider the following actions:
The importance of considering and informing people’s expectations about data use was emphasised during the Covid-19 pandemic.
The eighth Caldicott Principle reminds those using and sharing data of the idea of ‘no surprises’: the importance of considering and informing people’s expectations to promote understanding and agreement about its uses.
By sharing data you are being more open about the kind of information your organisation accesses, uses and shares. However, if this is a surprise to people, this could affect your reputation, and the trust they have in your organisation, so this will need to be managed.
" - } - ], - "examples": [ - { - "title": "Examples", - "text": "" - } - ], - "mitigating_actions": [ - { - "title": "Mitigating actions", - "text": "You can build or maintain a trustworthy reputation by undertaking the processes as follows:Clear and open communication can go a long way to help manage people's expectations. This might be about data practices – for example by publishing commitments, policies and approaches – being open about the types of data your organisation collects, uses and shares, and why (particularly for personal data) or direct engagement and events with key stakeholders.
Best-practice data sharing includes publishing well-structured, high-quality documentation and metadata to accompany data releases. Documentation can help users to understand important context – such as quality, when the data was collected, update schedules and limitations in collection or accuracy – to guide their use of the data. Data documentation that is open and clear can help users to understand whether they might be able to use it, can help to manage concerns around mis-use of data and help manage expectations around the data that may lead to reputational concerns. If you are sharing data, this guide on describing and documenting data well should help you to do this. If you are sharing a computer model, this guide on documenting and sharing models should help. There are also data quality frameworks available (for example UK government and ISO 19158) to help understand and communicate quality of the data for certain purposes.
Suppressing or redacting certain aspects of the data – like free text fields – through anonymisation techniques could help to minimise risk that could come from these fields being shared.
" - } - ], - "explain": [ - { - "exampleRisks": { - "title": "Potential risks:", - "items": [ - "Public backlash due to unexpected data sharing practices.", - "Loss of trust and credibility with stakeholders and the public.", - "Damage to the organisation's reputation from perceived data misuse.", - "Negative media coverage and scrutiny.", - "Unintended exposure of sensitive or controversial information.", - "Misinterpretation of data leading to misinformation.", - "Reduced engagement and cooperation from the community." - ] - }, - "exampleActions": { - "title": "Mitigating actions:", + "id": 7, + "title": "Will anyone be surprised by you holding, sharing or using this data?", + "text": "If the answer is Yes, it doesn\u2019t mean you can\u2019t share the data for reputational reasons. You can build or maintain a trustworthy reputation through clear and open communications about data practices in general, and about the data your organisation collects, uses and shares.", + "extra_text": "", + "category": "Reputational", + "options": [ + { + "option": "Yes", + "explain_risk": true, + "risk_level": "red" + }, + { + "option": "No", + "explain_risk": false, + "risk_level": "green" + }, + { + "option": "Uncertain", + "explain_risk": true, + "risk_level": "amber" + }, + { + "option": "N/A", + "explain_risk": false, + "risk_level": "green" + } + ], + "considerations": { + "title": "Have you considered:", "items": [ - "Engage the community and manage expectations around data use through transparent communication.", - "Describe and document the data well to provide context and clarity.", - "Anonymise the data to protect sensitive information.", - "Publish clear commitments, policies, and approaches regarding data practices.", - "Organise events and direct engagements with key stakeholders.", - "Encourage feedback and input to improve data practices and build trust.", - "Other?" + "informing people and get their agreement when shareing data with others?" ] - } + }, + "background_info": { + "title": "Background Information", + "text": "The importance of considering and informing people\u2019s expectations about data use was emphasised during the Covid-19 pandemic.
The eighth Caldicott Principle reminds those using and sharing data of the idea of \u2018no surprises\u2019: the importance of considering and informing people\u2019s expectations to promote understanding and agreement about its uses.
By sharing data you are being more open about the kind of information your organisation accesses, uses and shares. However, if this is a surprise to people, this could affect your reputation, and the trust they have in your organisation, so this will need to be managed.
" + }, + "examples": { + "title": "Examples", + "text": "" + }, + "mitigating_actions": { + "title": "Mitigating actions", + "text": "You can build or maintain a trustworthy reputation by undertaking the processes as follows:Clear and open communication can go a long way to help manage people's expectations. This might be about data practices \u2013 for example by publishing commitments, policies and approaches \u2013 being open about the types of data your organisation collects, uses and shares, and why (particularly for personal data) or direct engagement and events with key stakeholders.
Best-practice data sharing includes publishing well-structured, high-quality documentation and metadata to accompany data releases. Documentation can help users to understand important context \u2013 such as quality, when the data was collected, update schedules and limitations in collection or accuracy \u2013 to guide their use of the data. Data documentation that is open and clear can help users to understand whether they might be able to use it, can help to manage concerns around mis-use of data and help manage expectations around the data that may lead to reputational concerns. If you are sharing data, this guide on describing and documenting data well should help you to do this. If you are sharing a computer model, this guide on documenting and sharing models should help. There are also data quality frameworks available (for example UK government and ISO 19158) to help understand and communicate quality of the data for certain purposes.
Suppressing or redacting certain aspects of the data \u2013 like free text fields \u2013 through anonymisation techniques could help to minimise risk that could come from these fields being shared.
" + }, + "explain": { + "exampleRisks": { + "title": "Potential risks:", + "items": [ + "Public backlash due to unexpected data sharing practices.", + "Loss of trust and credibility with stakeholders and the public.", + "Damage to the organisation's reputation from perceived data misuse.", + "Negative media coverage and scrutiny.", + "Unintended exposure of sensitive or controversial information.", + "Misinterpretation of data leading to misinformation.", + "Reduced engagement and cooperation from the community." + ] + }, + "exampleActions": { + "title": "Mitigating actions:", + "items": [ + "Engage the community and manage expectations around data use through transparent communication.", + "Describe and document the data well to provide context and clarity.", + "Anonymise the data to protect sensitive information.", + "Publish clear commitments, policies, and approaches regarding data practices.", + "Organise events and direct engagements with key stakeholders.", + "Encourage feedback and input to improve data practices and build trust.", + "Other?" + ] + } } - ] }, { - "id": 8, - "title": "Does your organisation have the capability and knowledge to share the data safely?", - "text": "Ensuring that your organisation has the necessary capability and knowledge to share data safely is crucial for maintaining data integrity, protecting privacy, and upholding the organisation's reputation. A thorough assessment of your organisation's preparedness in terms of people, processes, and technology is essential for responsible data sharing.", - "extra_text": "", - "category": "Reputational", - "options": [ - { - "option": "Yes", - "explain_risk": false, - "risk_level": "green" - }, - { - "option": "No", - "explain_risk": true, - "risk_level": "red" - }, - { - "option": "Uncertain", - "explain_risk": true, - "risk_level": "amber" - }, - { - "option": "N/A", - "explain_risk": false, - "risk_level": "green" - } - ], - "considerations": { - "title": "Have you considered:", - "items": [ - "capabilities and knowledge of people?", - "processes and technology to share the data safely?", - "relevant legal support structures?", - "financial sustainability?" - ] - }, - "background_info": [ - { - "title": "Background Information", - "text": "It's important to consider if the people involved in processes that facilitate the sharing of data are appropriately trained and that you have a legal support structure in place. It is crucial that individuals involved in processes facilitating dataset sharing are appropriately trained to ensure competent and secure handling of data. Proper training enhances data management practices, promotes compliance with privacy regulations, and reduces the risk of errors or security breaches, fostering responsible and effective data sharing. It is essential that individuals understand the importance of safeguarding sensitive information, comply with data protection laws, uphold privacy standards and have the knowledge and skills needed to implement and follow robust security measures.
You will need to consider the capability and knowledge of your people, processes and technology in sharing data safely and responsibly.
Before the data is shared, you will also need to put in place a plan for maintaining the data and availability of this dataset to users over the frequency of publication previously identified. Ensuring that an organisation's infrastructure can support the creation, maintenance, and sharing of a dataset is crucial to guarantee seamless operations, data accuracy, and accessibility. A technical infrastructure is essential for managing data efficiently, implementing security measures, and facilitating a reliable and sustainable data sharing environment.
You will also need to ensure you have access to the funding needed to support the creation, maintenance and sharing of this dataset over the frequency of publication previously identified.
" - } - ], - "examples": [ - { - "title": "Examples", - "text": "" - } - ], - "mitigating_actions": [ - { - "title": "Mitigating actions", - "text": "It's important to consider if the people involved in processes that facilitate the sharing of data are appropriately trained and that you have a legal support structure in place. It is crucial that individuals involved in processes facilitating dataset sharing are appropriately trained to ensure competent and secure handling of data. Proper training enhances data management practices, promotes compliance with privacy regulations, and reduces the risk of errors or security breaches, fostering responsible and effective data sharing. It is essential that individuals understand the importance of safeguarding sensitive information, comply with data protection laws, uphold privacy standards and have the knowledge and skills needed to implement and follow robust security measures.
You will need to consider the capability and knowledge of your people, processes and technology in sharing data safely and responsibly.
Before the data is shared, you will also need to put in place a plan for maintaining the data and availability of this dataset to users over the frequency of publication previously identified. Ensuring that an organisation's infrastructure can support the creation, maintenance, and sharing of a dataset is crucial to guarantee seamless operations, data accuracy, and accessibility. A technical infrastructure is essential for managing data efficiently, implementing security measures, and facilitating a reliable and sustainable data sharing environment.
You will also need to ensure you have access to the funding needed to support the creation, maintenance and sharing of this dataset over the frequency of publication previously identified.
" + }, + "examples": { + "title": "Examples", + "text": "" + }, + "mitigating_actions": { + "title": "Mitigating actions", + "text": "Quality of data can be a big concern for organisations, especially when it comes to sharing data.
The level of quality required for each data set will vary depending on the purpose for which the data was collected, and will often consider several dimensions. For example, some decisions require up-to-date, complete and accurate data, whereas others are reliably informed by historic, aggregated data. Sharing data can help to improve its quality as people feedback on issues as they use it.
Overall, being open and welcoming input and feedback is essential to help build a healthy, trusted ecosystem around the data, and can help to maintain reputation.
Publishing documentation or a statement detailing the quality of a dataset is essential for transparency, enabling users to understand, assess and trust the reliability and accuracy of the data, ultimately supporting informed decision making and promoting data usability.
If there are any free-text or comment fields in the dataset, you will likely need a process in place to manage these, as by definition free-text fields are not restricted in value, they are input fields that can contain long notes, so could easily contain information not fit for wider consumption (for example descriptions, notes of conversations, opinions, actions, feedback – which can be of a personal or sensitive nature). Free-text or comment fields can also make it difficult to aggregate data in a way that it can be reused, due to a lack of standardisation or validation.
Overall you will need to think about your documentation, how to regularly assess the data, how you can receive feedback and respond to it.
" - } - ], - "examples": [ - { - "title": "Examples", - "text": "Supporting documentation or a quality statement is crucial to provide transparency and assurance regarding the reliability, accuracy, and overall quality of a dataset, aiding users in making informed decisions and fostering trust in the data.
Data quality dimensions serve as criteria for evaluating and gauging the overall excellence of data. Key dimensions such as accuracy, completeness, consistency, timeliness, relevancy and integrity are commonly employed to assess whether data aligns with specific standards and is suitable for its intended purpose. To uphold data quality, every organisation should establish methods for measuring and monitoring critical data items vital to its operations and assess them according to the relevant dimensions.
The UK 'Government Data Quality Hub' has some additional information about its data quality dimensions.
" - } - ], - "mitigating_actions": [ - { - "title": "Mitigating actions", - "text": "Quality of data can be a big concern for organisations, especially when it comes to sharing data.
The level of quality required for each data set will vary depending on the purpose for which the data was collected, and will often consider several dimensions. For example, some decisions require up-to-date, complete and accurate data, whereas others are reliably informed by historic, aggregated data. Sharing data can help to improve its quality as people feedback on issues as they use it.
Overall, being open and welcoming input and feedback is essential to help build a healthy, trusted ecosystem around the data, and can help to maintain reputation.
Publishing documentation or a statement detailing the quality of a dataset is essential for transparency, enabling users to understand, assess and trust the reliability and accuracy of the data, ultimately supporting informed decision making and promoting data usability.
If there are any free-text or comment fields in the dataset, you will likely need a process in place to manage these, as by definition free-text fields are not restricted in value, they are input fields that can contain long notes, so could easily contain information not fit for wider consumption (for example descriptions, notes of conversations, opinions, actions, feedback \u2013 which can be of a personal or sensitive nature). Free-text or comment fields can also make it difficult to aggregate data in a way that it can be reused, due to a lack of standardisation or validation.
Overall you will need to think about your documentation, how to regularly assess the data, how you can receive feedback and respond to it.
" + }, + "examples": { + "title": "Examples", + "text": "Supporting documentation or a quality statement is crucial to provide transparency and assurance regarding the reliability, accuracy, and overall quality of a dataset, aiding users in making informed decisions and fostering trust in the data.
Data quality dimensions serve as criteria for evaluating and gauging the overall excellence of data. Key dimensions such as accuracy, completeness, consistency, timeliness, relevancy and integrity are commonly employed to assess whether data aligns with specific standards and is suitable for its intended purpose. To uphold data quality, every organisation should establish methods for measuring and monitoring critical data items vital to its operations and assess them according to the relevant dimensions.
The UK 'Government Data Quality Hub' has some additional information about its data quality dimensions.
" + }, + "mitigating_actions": { + "title": "Mitigating actions", + "text": "Will the data be published with supporting documentation? For example:
Do you have processes and a supporting technical infrastructure in place to communicate updates about the dataset to your users? These are crucial to keep users informed, maintain data accuracy, and ensure the usability of shared information. It promotes transparency, allows users to stay current with the latest data versions, and facilitates effective collaboration by providing clear communication channels for any changes or enhancements to the dataset.
Do you have processes and a supporting technical infrastructure in place to collect feedback from users? These are vital for continuous improvement, user satisfaction, and effective data management. It allows organisations to gather insights, address user concerns, and enhance the dataset's quality, ensuring that it remains relevant and accurate, and meets the evolving needs of its user base.
Do you have processes in place to act on feedback? These are essential for ensuring that user input contributes to meaningful improvements in dataset quality and usability. It demonstrates responsiveness, enhances user satisfaction, and supports the continuous refinement of the dataset based on valuable insights provided by the user community.
Does the organisation have sufficient subject matter knowledge about the dataset being shared to be able to answer internal and external queries? This is important for the organisation to effectively address queries, both internal and external. This expertise ensures accurate and reliable responses, fosters transparency, and instills confidence in stakeholders, promoting a thorough understanding and responsible use of the shared data.
" - } - ], - "examples": [ - { - "title": "Examples", - "text": "Will the data be published with supporting documentation? For example:
Do you have processes and a supporting technical infrastructure in place to communicate updates about the dataset to your users? These are crucial to keep users informed, maintain data accuracy, and ensure the usability of shared information. It promotes transparency, allows users to stay current with the latest data versions, and facilitates effective collaboration by providing clear communication channels for any changes or enhancements to the dataset.
Do you have processes and a supporting technical infrastructure in place to collect feedback from users? These are vital for continuous improvement, user satisfaction, and effective data management. It allows organisations to gather insights, address user concerns, and enhance the dataset's quality, ensuring that it remains relevant and accurate, and meets the evolving needs of its user base.
Do you have processes in place to act on feedback? These are essential for ensuring that user input contributes to meaningful improvements in dataset quality and usability. It demonstrates responsiveness, enhances user satisfaction, and supports the continuous refinement of the dataset based on valuable insights provided by the user community.
Does the organisation have sufficient subject matter knowledge about the dataset being shared to be able to answer internal and external queries? This is important for the organisation to effectively address queries, both internal and external. This expertise ensures accurate and reliable responses, fosters transparency, and instills confidence in stakeholders, promoting a thorough understanding and responsible use of the shared data.
" + }, + "examples": { + "title": "Examples", + "text": "Commercially sensitive information is any information that requires more careful handling to reduce harmful impacts; it may require restricted access. This could include considerations around commercial confidentiality, intellectual property and competitors gaining an advantage.
What is considered confidential is usually decided by the organisation who created or stewarded that information and will depend on the context.
" - } - ], - "examples": [ - { - "title": "Examples of commercially sensitive data", - "text": "Commercially sensitive information is any information that requires more careful handling to reduce harmful impacts; it may require restricted access. This could include considerations around commercial confidentiality, intellectual property and competitors gaining an advantage.
What is considered confidential is usually decided by the organisation who created or stewarded that information and will depend on the context.
" + }, + "examples": { + "title": "Examples of commercially sensitive data", + "text": "Data has value when it flows while minimising harms. It is possible to minimise and mitigate risks. There is also a risk to the data ecosystem in not sharing data.
" - } - ], - "examples": [ - { - "title": "Examples", - "text": "Data has value when it flows while minimising harms. It is possible to minimise and mitigate risks. There is also a risk to the data ecosystem in not sharing data.
" + }, + "examples": { + "title": "Examples", + "text": "