Organisations have long recognised the commercial value of data. The value equation is impacted by the scale, speed, and sophistication at which AI is transforming. AI models can turn raw data into predictive intelligence, automated decisions, and entirely new revenue streams in ways that were not possible earlier. Enterprise leaders are no longer contemplating monetising data, but rather how to make it AI-ready, trusted, governed, and positioned to create compounding value across the organisation and the surrounding ecosystem.
Leading enterprises are already demonstrating what is possible. For example, insurance companies today supplement internal claims records with weather, geographical, and census data sourced from external providers to build machine learning models that anticipate future claim volumes. Retail and CPG companies are using shared retail data for business needs, such as category management, demand planning, and marketing analytics. These examples share a common prerequisite: the underlying data must be clean, current, and properly governed before it can fuel any AI application.
The readiness problem
Data volume is rarely the issue; the challenge lies in raw data residing in disconnected silos, inconsistently governed and formatted differently across systems, which makes it difficult to power AI models.
Regulatory compliance adds a further layer of complexity. India’s DPDP Act, Europe’s GDPR, and a range of sector-specific policy and compliance frameworks set conditions for the storage, processing, and sharing of personal data. In addition to the regulatory framework, these require technical controls baked into the data platform itself. Once tokenisation, role-based access, dynamic data masking, and end-to-end audit trails controls are in place, data becomes inherently more trustworthy and reliable.
From data sets to data products
Converting raw assets into shareable, monetisable data products starts with identifying the right datasets, which are assessed for demand, use-case fit, and regulatory compliance. Enriching those datasets with an analytics layer transforms static records into decision-ready intelligence, significantly increasing their value. But identifying what to share only gets an organisation so far. How data is distributed determines whether value is actually realised.
Zero Extraction-Transformation-Loading (ETL) enhances security and speed while derisking traditional data-movement approaches. Data remains securely within the provider’s environment, reducing the need for new pipelines. Authorised consumers can query it directly in real time without any physical transfer. There is no duplication, no latency, and no dilution of control.
This makes modern data marketplaces viable at scale, where providers expose exactly the data they choose to share, under the conditions they set, while retaining full ownership. External data marketplaces allow organisations to reach partners, customers, and third-party data providers across industries. Internal data marketplaces give different business units governed access to shared operational data without the friction of separate pipelines or duplicate datasets.
In one such example, a global consumer packaged goods company and a national retail chain shared point-of-sale and inventory data in a governed environment, pairing supplier production figures with live demand signals. Stockouts fell by 14%, revenue grew by 5%, and costs dropped significantly.
From data activation to decision advantage
Once governance and data-sharing foundations are in place, the real shift is from readiness to use. It is important to make data usable by pairing it with dashboards, notebooks, and logic that turn it into something organisations can act on directly. Generative AI is speeding this up. Providers can now expose AI-ready unstructured content, as searchable, RAG-enabled services through Knowledge Extensions, allowing consumers to integrate licensed unstructured content directly into their GenAI applications without moving data. This bridges the gap between “having data” and “having AI-ready knowledge”.
At the same time, the architecture itself is changing. For years, data was pushed toward applications. Now, that flow is reversing. Modern marketplaces today support connected applications that bundle application logic with data access, so a provider no longer just shares a dataset; they can distribute a complete analytical product that runs entirely in the consumer’s environment, without data ever leaving.
AI hasn’t changed the rules of competition so much as intensified them. Data is still central, but on its own, it’s no longer enough. The edge lies in combining it with built-in intelligence. As raw datasets become interchangeable, the real value lies in how effectively organisations turn them into decisions.
Authored by Dhiraj Narang, Director and Head of Partnerships, Snowflake, India