Indian Organisations Low In Data Maturity, Robust Datasets Needed For Agentic Future: EY
Indian organisations are still low in data maturity, the EY report said.
India is on the cusp of an artificial intelligence-driven revolution, but its success depends on building a robust data ecosystem that can support the growing demands of AI, according to a new report by consulting firm EY.
The report—'Data 4.0: Making your data AI-ready'— noted that high-quality, trustworthy data forms the backbone of AI-driven innovations. Without data maturity, the prototyping, deployment and effective testing of generative AI or any type of analytics become very challenging.
Indian organisations are still low in data maturity, it said. To harness the full potential of gen AI applications, businesses must balance technology, people and processes. Without structured datasets, the output of AI models can be flawed, leading to inaccuracies in decision-making, inefficiencies in operations and missed opportunities for growth.
“For India to realise its full potential as a global leader in AI innovation, we must prioritise building a robust data foundation. Data is the backbone that will enable AI systems to drive efficiency, economic growth and transformative change across industries. By investing in data readiness today, we are not just advancing technology, we are fuelling the future growth of our nation," said Alexy Thomas, technology consulting partner, EY India.
As India prepares for an ‘agentic future’ where AI systems grow increasingly autonomous, the quality, quantity and accessibility of datasets will define success. For AI agents to deliver reliable and valuable insights, they need access to well-organised, accurate and timely data.
The study outlined several measures Indian organisations should adopt to prepare their data for agentic AI systems, including:
Implementing Modern Data Infrastructure: Building flexible, scalable and secure data systems that can handle the complexity and volume of data required for AI systems to function autonomously.
Establishing Unified Data Access: Creating a unified data fabric, supported by a trusted data catalogue, that provides access to data across different platforms and ensures that AI agents can retrieve relevant information quickly and efficiently.
AI-Driven Data Governance: Automating data governance processes through AI-powered tools to ensure that data quality, compliance and privacy are maintained across the enterprise. This step is crucial in preventing data breaches and regulatory non-compliance.
Leveraging AI For Data Management: AI will not only be the consumer of data but also the tool for managing and governing data. Automating data cataloguing, metadata management and quality assurance will streamline the process of preparing data for AI applications.
Gen AI relies on a strong foundation of data maturity, which calls for an organisation to both integrate data through processes like moving and transforming it and manage its governance. As the country moves towards a future dominated by AI agents and autonomous systems, the right investments and strategies have the potential to drive economic growth and transform industries, the report added.
