Organizational and third-party data sharing have become even more important in recent years, especially with the exponential growth of data generated and stored within organizations. According to Statista, between 2020 to 2025, global data creation is projected to grow to more than 180 zettabytes. As organizations gather large amounts of data, the value of the data grows, and sharing valuable data can yield many benefits for them. However, sharing data has its limitations, such as potential privacy and security breaches. Today we will discuss the importance of data sharing (within the organization and third-party data sharing), traditional techniques used to share data, and the drawbacks of each of these techniques. Also, we will discuss the potential of synthetic data as a key enabler for fast, safe, and secure data sharing in the future.
Data sharing is a pivotal topic because it enables businesses to gain new insights and expand their knowledge in ways that may not be possible using their data. In financial services, data sharing can result in more effective risk management, fraud detection, and decision-making to improve customer experience. For example, credit card companies can share their transaction data with fraud teams across existing silos to understand consumer spending patterns, and identify potential fraud. Additionally, insurance companies can share their claims data to assess risk more effectively and optimize their policy pricing appropriately.
Sharing data with third-party vendors can provide opportunities for companies to gain access to new markets, identify growing trends and opportunities, and develop new products and services. Nevertheless, organizations must ensure that they are sharing data in a compliant and ethical manner, to protect the security of their customer data.
Third-party data sharing plays a crucial role in modern business strategies by offering companies access to new markets, enhanced capabilities for product development, and opportunities for innovation through collaboration. Unlike internal organizational data sharing, which primarily circulates data within an organization, third-party data sharing involves exchanging information with external entities, ranging from vendors and partners to independent researchers and other businesses.
One of the most significant benefits of third-party data sharing is the potential to tap into external expertise and technologies that can transform raw data into actionable insights, thereby driving business growth and innovation. For instance, a retail company might share customer shopping data with a marketing firm to create targeted advertising campaigns, or a healthcare provider might collaborate with a technology firm to analyze patient data and improve treatment plans.
However, third-party data sharing comes with its own set of challenges and complexities, primarily concerning security and compliance. Organizations must navigate a landscape filled with potential data breaches, privacy concerns, and stringent regulatory requirements such as GDPR in Europe or HIPAA in the United States. It is crucial to establish robust security measures like encryption, access controls, and regular audits to protect sensitive information from unauthorized access or leaks.
The methods employed for third-party data sharing are varied and must be chosen based on the specific needs and context of the data exchange. Common methods include:
To ensure legality and trust in third-party data sharing, it is imperative to draft comprehensive data-sharing agreements. These contracts should clearly outline the scope of data use, the responsibilities of each party, and the measures in place to protect data privacy and integrity. They also need to address compliance with relevant laws and regulations, which can vary significantly across different regions and industries.
By carefully managing these aspects, companies can leverage third-party data sharing to not only enhance their operational capabilities but also maintain compliance and uphold high standards of data privacy and security. This strategic approach allows for the expansion of business horizons while safeguarding the company's and customers' critical information.
The key techniques for organizational data sharing can be broken down into three main categories, including:
Synthetic data has emerged as a valuable tool to enable data sharing between silos. Tools such as the SDK are safer than using pseudonymization and information redaction techniques, but provide the same quality of data. Synthetic data produced by the SDK has no direct 1-to-1 mapping with the original data points and provides enhanced data protection features such as tunable differential privacy. This allows organizations to determine how much information is learned from any single row in their datasets by putting strict mathematical constraints on the models during training.
Synthesized’s Governor provides a platform for role-based access control to raw and synthetic data for internal data sharing, enabling easier and more transparent control of raw and synthetic data products internally.
Synthetic data is a valuable solution to enable cross-divisional data sharing within financial services. For example, a large bank with multiple business lines, such as commercial, investment, and retail banking is siloed into its divisions with little data sharing between them.
There are several reasons why it is difficult to share data between divisions within a financial services organization. Primarily, there can be concerns about data privacy and security risks, particularly when it comes to sharing sensitive customer information. This ties into the rapidly-changing landscape of regulatory and legal constraints, which restrict the sharing of particular types of data between divisions. Another reason why it may be difficult is that different divisions may use different technologies or data systems, which makes it difficult to integrate data from various sources.
The use of synthetic data can enable a division to generate a synthetic dataset that is representative of an original dataset. The synthetic datasets can be safely shared between divisions without risking the privacy of sensitive customer information. For example, the retail banking division of a large financial institution can generate synthetic data that is representative of customer behaviors, and this data can be shared with the institution's investment banking division to help inform investment decisions. This increases data access and data utility that is not achievable with original data.
Data possess tremendous value, and for financial services companies with vast amounts of data at their disposal, there is great potential for that data to be monetized. In recent years, there has been a growing discussion around the use of synthetic data for data monetization. The privacy-preserving features in synthetic data enable faster and safer sharing of information between silos, and to other data controllers such as third parties, without worrying about the restrictions that apply to sharing data containing personally identifiable information (PII).
Increased efficiency from the speed and improved safety of sharing synthetic data could allow for the creation of new revenue streams through the sale of synthetic versions of banking and insurance data to third parties. As the data monetization use case has become a more popular topic in the context of synthetic data, financial services companies have begun exploring its potential going forward.
Using synthetic data for data monetization could provide several business benefits such as:
However, there are challenges associated with using synthetic data for data monetization, including:
While synthetic data has potential benefits for data monetization, financial services firms must carefully consider the potential challenges and limitations before deciding to use it for this purpose.
Synthetic data’s use in academic research is increasing in importance due to its potential to overcome the problems associated with traditional data access and sharing. The use of synthetic data generation techniques enables researchers to generate data that is statistically representative and has the same correlations as the original data, without jeopardizing privacy or confidentiality. As a result, researchers have the opportunity to explore complex research questions that may have been difficult to address using original data alone.
For instance, in healthcare, the idea of using synthetic training data to help train machine learning models to detect early-stage lung cancer from CT scans, without compromising patient privacy and confidentiality when hospitals or medical researchers share the data has been highlighted.
Moreover, synthetic data can be used to address the issue of reproducibility in academic research, by allowing researchers to share data that can be used by other researchers without compromising the original data source. Nevertheless, the use of synthetic data in academic research and third-party data sharing provides a promising avenue for addressing the challenges of data access and sharing while advancing research in many fields.
To conclude, data sharing is pivotal for organizations that want to harness the potential of data to enhance decision-making, revenue, and innovation. But, data sharing does have significant risks, such as data privacy and security breaches, which can result in legal issues and significant costs. How data is leveraged for sharing must be cautious and considered thoroughly. Synthetic data is a promising enabler for secure data sharing, as it provides organizations with an opportunity to stay within the parameters of regulations. As synthetic data generation continues to evolve, organizations are likely to consider its use to enable fast, secure, and compliant data sharing.
Beyond the obvious risk of unauthorized access leading to data breaches, third-party data sharing introduces unique concerns. These include misuse of data for purposes not agreed upon in the contract, accidental data alteration by the third party's systems, and legal complications if the third party operates under different data protection regulations than your organization. It's crucial to meticulously vet potential partners and have robust agreements in place.
While synthetic data offers a promising solution for many third-party data sharing scenarios, it may not be a perfect substitute in every case. Highly sensitive data or scenarios requiring real-time updates might still necessitate some level of direct sharing. However, synthetic data significantly reduces risk and can be the preferred method for many use cases, particularly when combined with other privacy-enhancing techniques.
Third-party data sharing is revolutionizing data monetization. It opens up new avenues for generating revenue by securely sharing data with external parties interested in insights or analytics. This could involve selling anonymized datasets, offering API access to specific data streams, or partnering with other organizations for joint data-driven projects. Synthetic data further amplifies this potential, allowing for secure monetization without compromising sensitive information.
Thorough due diligence is crucial when selecting third-party data sharing partners. This includes assessing their data security practices, track record with data handling, compliance with relevant regulations, and the robustness of their data sharing agreements. Third-party certifications (like ISO 27001) can provide an added layer of assurance. Additionally, organizations should continuously monitor their partners' performance and have clear exit strategies in case the relationship doesn't meet expectations.
