How do you weigh data sharing risks against benefits?

When weighing data sharing risks versus benefits, I consider core factors like purpose, consent, anonymization, protections, harm assessments, and governance oversight. Data should only be shared in an ethical manner that genuinely empowers people and minimizes exploitation risks. If sensible governance, security controls, and privacy safeguards cannot sufficiently reduce potential harms, restraint is warranted. Trust should be placed first. Ultimately, data must advance human dignity - if risks outweigh that principle, caution is needed, even if benefits seem apparent on the surface.

Data is a power instrument to take better decisions, sharing data provides from big populations turns the daily life/work better.

Data means little without Analytics. Business and Engineering need to work closer than ever to derive the most efficiencies and insights for any business. Because, more efficiency and more insights, eventually translates to better bottom lines.

In the field of blockchain, working with financial datasets always posed a challenge with regards to data privacy. Fortunately, the advent of Zero-knowledge or ZK technology has enabled data analysts to access these datasets without compromising privacy. This revolutionary technology has allowed healthcare and financial companies to offer products and solutions while ensuring that privacy is maintained. With ZK technology, the data sets remain immutable, yet analysts can still leverage data points.

Should also take into account the cost or replacement, for example collecting field inspection information. Cost of the data should include the time it's taken for someone to physcially collect. Should also not overlook the value of data to others, for your organisation it might be "worthless" but other organisations would love to have access to that dataset for their insights.

Data sharing should be integrated with blockchain to keep track or monitor where it's going, where it's gone, where it will be in the next few months. This will definitively allow originator/owner of data to be aware who potentially find value in it and how it will be manipulated or used to create advantages by industry players.

"How will data sharing expose your data to unauthorized access, misuse, or loss?" - This should really be expanded. really need to consider if someone uses your "open-data" as the basis of design assumptions for a building, building falls over, is your organisation liable? Need to really consider the potential use cases of datasets you're looking to share and ensure that your data license covers your organisation against this.

To weigh data sharing risk against benefits: Identify purpose and goals. Assess benefits and quantify if possible. Analyze security and privacy risks. Ensure legal compliance. Minimize sensitive data shared. Implement security and privacy measures. Use data sharing agreements. Monitor and adapt continuously.

Before sharing data, it's important to consider the risk of re-identification. Numerous studies have shown that removing personal information from a dataset is not always enough to prevent re-identification of individuals. This is due to several factors, including the prevalence of "big data" and the use of increasingly sophisticated algorithms. The best example of this is a study ("Who's Watching?") about the Netflix Prize dataset, which contained users' ratings of movies. By comparing this with another dataset of product reviews from Amazon, researchers were able to identify "anonymized" Netflix users by matching them to their Amazon user profiles based on the similarity and temporal proximity of movie reviews across the two datasets.

Data sharing helps to collaborate better among different parties for business work. The sharing capabilities should align with organizational overall governance. Data is shared with unauthorized or no time limits will create disasters. Different key stakeholders in business needs to evaluate the data sharing policies they are cyber security group, Data governance committee and alignment with Records and privacy managers along with IT operations group. Proper monitoring and right data sharing that suffies the business case adds value to overall data sharing experience.

Balancing transparency with security is often the tightrope we walk in data sharing. One insightful realization I had was that the culture and ethos of an organization play a significant role in determining its data-sharing level. In organizations where trust and collaboration are foundational values, there's a natural inclination toward more open data sharing. However, it's vital to continuously reassess the appropriateness of this sharing level, especially as external factors and organizational priorities evolve.

Striking the right balance between sharing and protecting sensitive information is paramount. While data sharing offers numerous benefits, safeguarding personal information should always be a top priority. I think one effective strategy is to implement proper encryption for sensitive data. Encryption ensures that even if data is shared, it remains indecipherable to unauthorized parties, adding an extra layer of security. This approach enables organizations to share valuable insights and collaborate with stakeholders while mitigating the risks associated with data breaches or misuse. By adopting encryption techniques, we can promote responsible data sharing practices, fostering trust and accountability in the digital age.

- In my experience in working with highly restricted data, it is important to understand the distinction between what a stakeholder is requesting and what you’re legally able to provide them. For auditing purposes, I output two reports. One internal report with restricted fields and a summarized version with the appropriate information.

I can see that Restrict data must be practiced to encourage advancement of medical field with hopes of improving longevity of people as people are essentially the workers, the customers, the voters, business owners etc.

I would not sign up for this. Now closed is as open as open. You have to mitigate the high risk of closed going open. In my opinion you need to always consider data theft and unethical misuse.

To establish unbiased governance over a decentralized global database, I think the creation of an independent, international consortium comprising representatives from governments, academia, industry, and civil society can come into play. This entity should be committed to transparency, inclusivity, and equitable data access. It must define clear data-sharing protocols, enforce robust privacy standards, and ensure equal participation for all stakeholders. Regular audits and reviews would maintain accountability. Ultimately, this global consortium would serve as a guardian of data ethics, fostering trust and fair data utilization worldwide.

Best if at first we find best use of own data before sharing. It is easy to see, that volumes of books worth data is generated in a monthly base at a mid size company. Read your own books first.

A well documented data set is easier to use and more likely to be utilized. Look at other data sets that you've found useful - what information was included? On the other hand, what information was left out from data sets that you found challenging to analyze? The documentation you create also must be easily discoverable in order to be useful. Clearly link to all documentation every time you post or share your data set.

Building a culture of trust is foundational to successful data sharing. Even with all the technical safeguards in place, if stakeholders don't trust the process or the entities they're sharing data with, the initiative is likely to face resistance. It's essential to prioritize transparent communication about how data will be used, who has access, and the benefits of the sharing process. This ensures that all parties involved are aligned, fostering a collaborative and transparent data-sharing environment.

1. Implementing robust Data Clean Rooms is the way to go for situations when data has to be shared between multiple organisations for enrichment, enhanced Analytics, etc. 2. Modern day tools and platforms like Snowflake, makes the workflow of implementing a Data Clean Room solution like a piece of cake. 3. The key aspects that are to be considered while sharing data such as Security & Governance, Compliance/Regulatory needs, etc. can be ensured pretty easily and quickly, without compromising the quality on Snowflake based on my experience. Out of the box Masking & Row level security functionalities of Snowflake come super handy in these scenarios.

Don’t fall for standards in data sharing. The hackers are way ahead of the regulators. Protect your data assets by all means and make the most of them on your own.

Beyond the tools and formal guidelines, there's immense value in joining data-sharing communities or forums. Interacting with peers who have faced similar challenges provides a real-world perspective that often goes beyond what's captured in official documentation. Such interactions not only offer solutions to specific problems but also help in understanding the nuances and cultural aspects of data sharing across different industries and regions.

- Before sharing any data, weigh the potential risks against the benefits. This can include anything from data leaks to the misuse of sensitive information. - Use encryption and secure channels for transferring data. The more sensitive the data, the higher the level of security you should employ. - Always be upfront about how the data will be used, who will have access to it, and what measures are in place to protect it. - The world of data sharing is ever-evolving. Stay updated with the latest trends, technologies, and regulations to ensure you're always in compliance.

- Study successful cases where data sharing led to positive outcomes. - Learn from the mistakes of others to avoid common pitfalls in data sharing. - Stay updated with resources like industry publications, online courses, and conferences to enhance your knowledge.

When it comes to data sharing, learning from examples and utilizing available resources is key. Explore different contexts and domains to understand the advantages and disadvantages of data sharing. Familiarize yourself with the FAIR principles, the Data Sharing Toolkit, the Data Collaboratives Explorer, the Responsible Data Forum, and the Data Ethics Canvas. These resources provide valuable insights and guidance for making informed and ethical decisions. Remember, data sharing is a complex process that requires careful consideration and balance. By leveraging these tips, you can navigate the risks and benefits of data sharing effectively.

Consent and transparency Consent and transparency are cornerstones of responsible data sharing; ensuring that individuals are both well-informed and have provided their consent for data sharing is not just a legal requirement but an ethical obligation. Alongside this, practitioners should contemplate data minimization, pondering whether only necessary data is shared, as this can significantly mitigate risks. The practice of data anonymization or pseudonymization also merits scrutiny. Assessing whether data has been suitably anonymized or pseudonymized to protect individual privacy adds a layer of protection against potential harms.

Applying the principle of least privilege (PoLP) is my preferred way to go about data and information sharing. This principle demands that a user is only given access to specific information and resources needed to complete only the required task, and not beyond that. In some cases, a user may require high-level access where instead of "upgrading" their user role or access privilege you may implement mechanisms/tokens in which the requested access can only be granted temporarily for a specified period of time before the access is timed out.

When weighing data sharing risks against benefits, it's essential to consider the potential advantages and drawbacks. Assess the value of the benefits you'll gain from sharing data, such as personalized experiences or improved services, and compare them to the potential risks, like privacy concerns or data breaches. It's important to make informed decisions and prioritize your comfort level with sharing personal information. Remember to review privacy policies and take necessary precautions to protect your data.

Data alone can be overwhelming. To ensure shared data makes sense and drives the right impact, we must focus on context and storytelling. Start by understanding your audience's needs and goals. Then, present data with a clear narrative that connects the dots. Use visuals for clarity. Avoid jargon and keep it concise. Invite questions and discussions to foster understanding. Most importantly, link data to actionable insights. When data is presented in a way that resonates with your audience and guides decision-making, it becomes a powerful tool for informed choices and meaningful impact. Refresh and re-align with the agreed metrics and KPIs in the org, of course do not forget to keep consent and data privacy concerns in mind before sharing!

Data owners often operate in multiple jurisdictions, each with varying laws. Navigating these inconsistent regulations makes them too cautious, leading many to withhold data to stay legally compliant. Even when laws permit sharing, the fear persists. Consequently, they miss out on the advantages of data sharing, sometimes even within their own cross border businesses, limiting business intelligence. This reluctance hampers the development of thr data professional community and 3rd party data science firms that help businesses unlock data value. We as data professionals also need to invest more time and effort to understand different regulations and other qualitative topics about data. Our focus has largely been on the technical side