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The article emphasises the strategic value of data privacy in driving business innovation and profitability. It highlights the transformative role of these technologies in enabling ethical AI use, innovative data monetisation and secure data transactions. By leveraging privacy as a competitive advantage and an asset, businesses can unlock new growth opportunities, mirroring the successes achieved with advanced cybersecurity solutions. The focus shifts from viewing data privacy merely as a compliance requirement to recognising it as a key driver of sustainable business success.
Privacy is often viewed through the narrow lens of compliance and consumer protection. However, this perspective overlooks the strategic value that privacy can bring to the business table. It's time to challenge the prevailing narrative and explore privacy as a critical asset that goes beyond legal obligations.
This article aims to shift the focus towards the untapped potential of privacy as a driver for innovation, operational excellence and competitive advantage. By reimagining privacy as an integral part of business strategy, organisations can unlock new avenues for growth and profitability that extend well beyond the traditional boundaries of data protection and data security.
Now, distinguishing your business from rivals requires more than just innovative products or services; it demands a strategic approach to privacy. Beyond compliance, privacy has emerged as a potent competitive differentiator, particularly in B2B relationships and partnerships where trust and reliability are important.
Businesses that champion privacy not only stand out in a crowded market but also build stronger partnerships, as they are seen as more trustworthy. This perception is invaluable in sectors where data sensitivity is high and discretion is a cornerstone of business operations.
This emphasis on privacy can attract partners and clients who value data security, opening doors to markets that were previously inaccessible due to stringent privacy standards.
This is no longer about avoiding penalties or meeting the minimum legal requirements; it's about leveraging privacy as a hallmark of business excellence. Companies that proactively adopt and promote strong privacy measures can navigate the market with a clear advantage, positioning themselves as leaders in a landscape where data security is increasingly becoming a priority for businesses worldwide.
This strategic pivot towards privacy not only enhances reputational standing but also aligns with a broader business vision that prioritises long-term sustainability over short-term gains.
By embedding privacy into their operational ethos, businesses can catalyse a shift in industry standards, encouraging a move towards more ethical and responsible data management practices.
Data monetisation extends beyond traditional data selling, focusing on leveraging data to unlock new revenue models and value propositions without compromising privacy.
It encompasses using data in ways not originally collected or intended for, by applying privacy-preserving technologies to create new opportunities for business and societal benefit while remaining compliant.
By leveraging techniques like differential privacy, federated learning and secure multiparty computation, companies could maximise data utility by selling or sharing non-personal or privacy-enhanced datasets to other companies to use as they please.
This could result in the development of a product or the discovery of a solution that benefits society as a whole and hasn’t sacrificed the privacy of individuals.
During the unprecedented times of the COVID-19 pandemic, the principle of using data innovatively while preserving privacy was put to the test. Openmined emerged as a frontrunner, offering open-source libraries that enabled organisations to integrate privacy into the development of their contact tracing applications.
They argued that for contact tracing to be effective, it needed widespread adoption and trust, which could only be achieved by ensuring privacy through privacy-centric technologies - in this case, Private Set Intersection, a type of Secure Multiparty Computation, was used to compare the user’s location data with the patient’s location data to determine whether they were at risk.
In this use case, the data wasn’t monetised and (we believe) no one profited from the COVID-19 Apps - but the application of the idea remains valid. By using privacy-enhancing technologies, businesses can leverage their data to generate new use cases, value propositions and revenue streams that maximise data utility and minimise data privacy risks.
Two techniques in particular, Secure Multiparty Computation and Federated Learning represent potential paradigm shifts in data monetisation through privacy preservation.
Secure Multiparty Computation (SMPC) and in particular Private Set Intersection (PSI), can enable secure data analysis and monetisation.
PSI allows entities to determine the intersection of their datasets, revealing only the common elements without exposing the distinct elements of each set. This capability is invaluable in fields like healthcare, finance and cybersecurity.
For instance, two competing healthcare organisations could use PSI to jointly study the prevalence of a rare disease in their patient populations without revealing identifiable patient information.
This method not only preserves privacy but also opens up new avenues for data-driven collaboration and innovation, turning data into a non-zero-sum asset where all parties can gain insights and create value.
An example of PIS in action is Apple’s Password Monitoring feature. This feature helps users identify if their saved passwords have been involved in a data breach without compromising the privacy of their password information.
This is achieved by comparing a user's passwords against a list of known compromised passwords in a way that neither Apple nor any other party can see the passwords. This demonstrates how privacy and security can be enhanced simultaneously, offering users valuable insights into their digital security posture.
One particular use case for SMPC and PIS, in particular, is within Financial Services and FinTech.
Imagine two banks, Bank A and Bank B, each suspecting fraudulent activity on their networks. They both have lists of potentially fraudulent transactions or suspicious user accounts. However, neither wants to share their complete lists due to concerns about competitor analysis or revealing information about their customers.
They can harness Private Set Intersection (PSI) to tackle fraud collaboratively while safeguarding customer privacy. By comparing anonymised lists of potentially fraudulent transactions or accounts, PSI allows them to identify shared threats without revealing any individual details.
This enables the banks to swiftly flag high-risk activity indicative of wider fraud schemes targeting multiple institutions. Crucially, neither bank learns anything beyond the existence of matching entries, ensuring both customer privacy and coordinated action.
This collaborative approach, fueled by PSI, empowers banks to combat fraud effectively while fostering a more secure financial ecosystem.
This technique enables companies to derive insights from decentralised data sources without ever having to pool personal data into a single location.
It allows multiple stakeholders to contribute to the development of more robust, generalised machine learning models while ensuring that each participant's data remains on their device, protected and private.
One particular use case that could have profound implications is the use of Federated Learning in healthcare settings. Using this technique, organisations can license their clinical data to researchers which creates a new revenue stream for the organisations and provides HIPAA and GDPR-compliant access to data that would otherwise be off-limits.
One example is the EXAM model created during the COVID-19 pandemic. EXAM combined the data from 20 hospitals across five continents to train a neural network that successfully predicted the level of supplemental oxygen a COVID-19 patient may need 24 and 72 hours after arriving at the hospital.
They trained the model over two weeks and achieved high-quality predictions, generating insights that helped physicians determine the level of care required. In further testing on data the model had never seen before, they found that EXAM predicted ventilator need within 24 hours of the patient presenting with a sensitivity of 95% and a specificity of over 88%.
The traditional view that privacy protection limits data utility is being challenged by privacy-enhancing technologies and the view that maintaining privacy doesn’t have to come at a cost.
By leveraging these techniques, companies can transform their data into valuable, non-personal datasets that retain their utility for analysis and decision-making. This opens up new opportunities which primarily create revenue and drive innovation while also maintaining regulatory compliance.
Now, we’re going to take a look at a case study in cybersecurity that demonstrates how adopting new technologies can drive business growth.
GMG, facing increased cyber threats during its expansion, significantly enhanced its cybersecurity by adopting CrowdStrike Falcon Complete MDR, achieving a 96% reduction in incident remediations.
Selecting CrowdStrike for its comprehensive 24/7 managed detection and response, GMG efficiently deployed the solution across 3,500 endpoints, ensuring end-to-end protection with minimal impact on operations.
Key Outcomes:
Key Takeaway
GMG's investment in CrowdStrike's cybersecurity solutions resulted in immediate security enhancements, cost savings, and protection of its brand. It also aligned with the company's business objectives, showcasing the value of advanced cybersecurity in the digital era.
We know that businesses have always leveraged technology to fuel growth and efficiency. Now, the spotlight turns to data privacy and risk management technologies as key drivers for the next era of business innovation. The strategic adoption of these technologies offers several opportunities:
Data privacy and risk management technologies are poised to replicate the transformative impact on business growth previously seen with cybersecurity solutions.
By prioritising these technologies, businesses can harness the strategic benefits of privacy beyond compliance— driving innovation, monetising data and creating value in privacy-focused markets—thereby driving profitability and sustainable success in a data-centric world.
In today’s world, data is considered both an asset and a liability. Redefining how you approach data privacy presents a strategic opportunity to enable the next phase of business growth in a society that is paying more attention to what is done with the data they are given.
Data monetisation goes beyond selling raw data - it encompasses compliant use of that data for new purposes and, by prioritising privacy, businesses can drive innovation, gain a competitive advantage and create new revenue streams using an existing asset.
The focus is now shifting from data privacy as a compliance requirement to data privacy as a business enabler. Investing in data privacy and privacy-enhancing technologies isn’t just about mitigating risks anymore, it’s about seizing opportunities.
Businesses that recognise and act on the opportunities will not only stay ahead of the regulatory curve but will also drive a future where privacy and profitability are intrinsically linked.
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The article emphasises the strategic value of data privacy in driving business innovation and profitability. It highlights the transformative role of these technologies in enabling ethical AI use, innovative data monetisation and secure data transactions. By leveraging privacy as a competitive advantage and an asset, businesses can unlock new growth opportunities, mirroring the successes achieved with advanced cybersecurity solutions. The focus shifts from viewing data privacy merely as a compliance requirement to recognising it as a key driver of sustainable business success.
Privacy is often viewed through the narrow lens of compliance and consumer protection. However, this perspective overlooks the strategic value that privacy can bring to the business table. It's time to challenge the prevailing narrative and explore privacy as a critical asset that goes beyond legal obligations.
This article aims to shift the focus towards the untapped potential of privacy as a driver for innovation, operational excellence and competitive advantage. By reimagining privacy as an integral part of business strategy, organisations can unlock new avenues for growth and profitability that extend well beyond the traditional boundaries of data protection and data security.
Now, distinguishing your business from rivals requires more than just innovative products or services; it demands a strategic approach to privacy. Beyond compliance, privacy has emerged as a potent competitive differentiator, particularly in B2B relationships and partnerships where trust and reliability are important.
Businesses that champion privacy not only stand out in a crowded market but also build stronger partnerships, as they are seen as more trustworthy. This perception is invaluable in sectors where data sensitivity is high and discretion is a cornerstone of business operations.
This emphasis on privacy can attract partners and clients who value data security, opening doors to markets that were previously inaccessible due to stringent privacy standards.
This is no longer about avoiding penalties or meeting the minimum legal requirements; it's about leveraging privacy as a hallmark of business excellence. Companies that proactively adopt and promote strong privacy measures can navigate the market with a clear advantage, positioning themselves as leaders in a landscape where data security is increasingly becoming a priority for businesses worldwide.
This strategic pivot towards privacy not only enhances reputational standing but also aligns with a broader business vision that prioritises long-term sustainability over short-term gains.
By embedding privacy into their operational ethos, businesses can catalyse a shift in industry standards, encouraging a move towards more ethical and responsible data management practices.
Data monetisation extends beyond traditional data selling, focusing on leveraging data to unlock new revenue models and value propositions without compromising privacy.
It encompasses using data in ways not originally collected or intended for, by applying privacy-preserving technologies to create new opportunities for business and societal benefit while remaining compliant.
By leveraging techniques like differential privacy, federated learning and secure multiparty computation, companies could maximise data utility by selling or sharing non-personal or privacy-enhanced datasets to other companies to use as they please.
This could result in the development of a product or the discovery of a solution that benefits society as a whole and hasn’t sacrificed the privacy of individuals.
During the unprecedented times of the COVID-19 pandemic, the principle of using data innovatively while preserving privacy was put to the test. Openmined emerged as a frontrunner, offering open-source libraries that enabled organisations to integrate privacy into the development of their contact tracing applications.
They argued that for contact tracing to be effective, it needed widespread adoption and trust, which could only be achieved by ensuring privacy through privacy-centric technologies - in this case, Private Set Intersection, a type of Secure Multiparty Computation, was used to compare the user’s location data with the patient’s location data to determine whether they were at risk.
In this use case, the data wasn’t monetised and (we believe) no one profited from the COVID-19 Apps - but the application of the idea remains valid. By using privacy-enhancing technologies, businesses can leverage their data to generate new use cases, value propositions and revenue streams that maximise data utility and minimise data privacy risks.
Two techniques in particular, Secure Multiparty Computation and Federated Learning represent potential paradigm shifts in data monetisation through privacy preservation.
Secure Multiparty Computation (SMPC) and in particular Private Set Intersection (PSI), can enable secure data analysis and monetisation.
PSI allows entities to determine the intersection of their datasets, revealing only the common elements without exposing the distinct elements of each set. This capability is invaluable in fields like healthcare, finance and cybersecurity.
For instance, two competing healthcare organisations could use PSI to jointly study the prevalence of a rare disease in their patient populations without revealing identifiable patient information.
This method not only preserves privacy but also opens up new avenues for data-driven collaboration and innovation, turning data into a non-zero-sum asset where all parties can gain insights and create value.
An example of PIS in action is Apple’s Password Monitoring feature. This feature helps users identify if their saved passwords have been involved in a data breach without compromising the privacy of their password information.
This is achieved by comparing a user's passwords against a list of known compromised passwords in a way that neither Apple nor any other party can see the passwords. This demonstrates how privacy and security can be enhanced simultaneously, offering users valuable insights into their digital security posture.
One particular use case for SMPC and PIS, in particular, is within Financial Services and FinTech.
Imagine two banks, Bank A and Bank B, each suspecting fraudulent activity on their networks. They both have lists of potentially fraudulent transactions or suspicious user accounts. However, neither wants to share their complete lists due to concerns about competitor analysis or revealing information about their customers.
They can harness Private Set Intersection (PSI) to tackle fraud collaboratively while safeguarding customer privacy. By comparing anonymised lists of potentially fraudulent transactions or accounts, PSI allows them to identify shared threats without revealing any individual details.
This enables the banks to swiftly flag high-risk activity indicative of wider fraud schemes targeting multiple institutions. Crucially, neither bank learns anything beyond the existence of matching entries, ensuring both customer privacy and coordinated action.
This collaborative approach, fueled by PSI, empowers banks to combat fraud effectively while fostering a more secure financial ecosystem.
This technique enables companies to derive insights from decentralised data sources without ever having to pool personal data into a single location.
It allows multiple stakeholders to contribute to the development of more robust, generalised machine learning models while ensuring that each participant's data remains on their device, protected and private.
One particular use case that could have profound implications is the use of Federated Learning in healthcare settings. Using this technique, organisations can license their clinical data to researchers which creates a new revenue stream for the organisations and provides HIPAA and GDPR-compliant access to data that would otherwise be off-limits.
One example is the EXAM model created during the COVID-19 pandemic. EXAM combined the data from 20 hospitals across five continents to train a neural network that successfully predicted the level of supplemental oxygen a COVID-19 patient may need 24 and 72 hours after arriving at the hospital.
They trained the model over two weeks and achieved high-quality predictions, generating insights that helped physicians determine the level of care required. In further testing on data the model had never seen before, they found that EXAM predicted ventilator need within 24 hours of the patient presenting with a sensitivity of 95% and a specificity of over 88%.
The traditional view that privacy protection limits data utility is being challenged by privacy-enhancing technologies and the view that maintaining privacy doesn’t have to come at a cost.
By leveraging these techniques, companies can transform their data into valuable, non-personal datasets that retain their utility for analysis and decision-making. This opens up new opportunities which primarily create revenue and drive innovation while also maintaining regulatory compliance.
Now, we’re going to take a look at a case study in cybersecurity that demonstrates how adopting new technologies can drive business growth.
GMG, facing increased cyber threats during its expansion, significantly enhanced its cybersecurity by adopting CrowdStrike Falcon Complete MDR, achieving a 96% reduction in incident remediations.
Selecting CrowdStrike for its comprehensive 24/7 managed detection and response, GMG efficiently deployed the solution across 3,500 endpoints, ensuring end-to-end protection with minimal impact on operations.
Key Outcomes:
Key Takeaway
GMG's investment in CrowdStrike's cybersecurity solutions resulted in immediate security enhancements, cost savings, and protection of its brand. It also aligned with the company's business objectives, showcasing the value of advanced cybersecurity in the digital era.
We know that businesses have always leveraged technology to fuel growth and efficiency. Now, the spotlight turns to data privacy and risk management technologies as key drivers for the next era of business innovation. The strategic adoption of these technologies offers several opportunities:
Data privacy and risk management technologies are poised to replicate the transformative impact on business growth previously seen with cybersecurity solutions.
By prioritising these technologies, businesses can harness the strategic benefits of privacy beyond compliance— driving innovation, monetising data and creating value in privacy-focused markets—thereby driving profitability and sustainable success in a data-centric world.
In today’s world, data is considered both an asset and a liability. Redefining how you approach data privacy presents a strategic opportunity to enable the next phase of business growth in a society that is paying more attention to what is done with the data they are given.
Data monetisation goes beyond selling raw data - it encompasses compliant use of that data for new purposes and, by prioritising privacy, businesses can drive innovation, gain a competitive advantage and create new revenue streams using an existing asset.
The focus is now shifting from data privacy as a compliance requirement to data privacy as a business enabler. Investing in data privacy and privacy-enhancing technologies isn’t just about mitigating risks anymore, it’s about seizing opportunities.
Businesses that recognise and act on the opportunities will not only stay ahead of the regulatory curve but will also drive a future where privacy and profitability are intrinsically linked.
