Artificial Intelligence is a game changer that is set to transform the business world and Data Trusts have become an indispensable part of digital adoption and scalability of operations for the new age companies. Use of qualitative data provides a wide range of benefits in the areas of financial services, health care industry with the help of digital healthcare solutions.

However accessing data and sharing them with other players in the market poses significant risks to all the stakeholders involved due to breach of confidentiality, privacy resulting in regulatory and compliance, financial and reputational risks. In order to mitigate this risk in data sharing and provide a robust framework and governance in managing data, companies are coming up with a new solution in the form of a data trust that is expected to institutionalise data sharing through well-defined protocol. The data trust would act as the administrator that manages and oversees the operations of data management like data collection, processing, access and utilisation.

Data interoperability helps in sharing and exchange of data across different service providers that enables consumption of data through shared expectations in context, content and meaning. A common error like data bias could lead to undesired outcomes resulting in poor decision making and biased reporting. A strategic formation of minimal viable coalition promotes data interoperability by removing data bias and usage of data identification techniques. It also helps in identification of any challenges faced such as legal ethical barriers and focuses on specific use cases by sharing knowledge and resources and leveraging on latest technologies like federated learning, distributed ledgers.

Creation of day trust co-operatives would be essential to build accountability and safeguard the interests of the trust by ensuring unalloyed loyalty and discharging its duties and responsibilities through appropriate checks and balances for promoting collective action and inclusivity. This would help in building a healthy relationship between different data providers and consumers resulting in financially sustainable trusts providing tangible economic benefits through data monetisation and storage. The data property and copyright laws would set the standards for data ownership in the data value chain based on roles and responsibilities of different stakeholders.

Data Trusts helps in boosting creativity and fostering a culture of innovation by enabling digital equity and collaboration. A regulatory framework is required for calibration of datasets as financial assets that would help in trading of data assets resulting in economic benefits and mutually rewarding experience for the data trusts. Data Trusts enable democratisation of AI resulting in reduction of entry barriers and promoting a level playing field for businesses to maximize their value through dedicated training and governance.

Conclusion
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