Data 4.0: how to make your enterprise data AI-ready

Related topics

To unlock GenAI-led transformation, Indian organizations have to build AI-ready data.


In brief

  • Data maturity is critical for effective AI implementation
  • Data 4.0 focuses on AI-powered, cloud-native architectures
  • A robust, flexible data foundation is essential for organizations to be ready for an agentic future

As Indian organizations adopt artificial intelligence (AI) to transform their businesses, success depends not only on sophisticated algorithms or computing power but also on the quality of data, the backbone of decision-making, customer interactions and operational efficiency. According to the EY report, Data 4.0: Making your data AI-ready without high-quality, trustworthy data and data maturity, prototyping, testing and effective deployment of generative AI (GenAI) and data analytics solutions become difficult.

Many Indian organizations are implementing or plan to implement AI tools but face the challenge of low data maturity. Poor-quality data can lead to inaccurate results and affect the speed of AI implementation, which often depends on the availability of current, relevant datasets. Organizations that build an open and trusted data foundation will gain a competitive advantage in an AI-driven future. 

Building AI-ready data

A robust data foundation leads to AI models that produce reliable results. Therefore, AI data preparation, storage, management, and accessibility across hybrid, cloud and on-premises environments are crucial for enterprises that aim to enhance productivity, innovate and create new revenue models. Companies can take a domain-driven approach – which involves categorizing data and AI capabilities into logical domains that align with business functions – as it helps in effectively managing data and AI initiatives across the organization. 

How to make your enterprise data AI-ready

To create a robust foundation for AI implementation, organizations should consider the following seven pillars:

The seven-pillar AI-ready data framework

  • AI-ready data strategy:  An adaptive and effective AI data strategy harnesses data value aligns with business goals, and bridges data capabilities with AI objectives. Regular technology updates help in ensuring maximization of potential.
  • Knowledge management: Efficient knowledge management platforms are adaptable and capable of integration with LLMs. This might necessitate data restructuring to help ensure compatibility and effective usage within the LLM knowledge base.
  • Data governance:  AI-ready data must be accessible, self-defining and convey its constraints. Enhancing metadata cataloguing, data product handling and automating data access monitoring and provisioning are vital.
  • Master data management for GenAI: AI-ready environments offer a superior context for transaction data. With master data serving as the GenAI context, it is vital to establish a single, reliable source for entities tied to all transaction data.
  • Data risk and compliance: Data products with automated risk and compliance controls are vital. For a quick AI adoption, robust, automated data controls are needed for data sovereignty, data privacy and compliance to regulatory requirements.
  • AI Data quality:  Not all data in an organization is equally significant. Critical data elements demand resource allocation and observability in appropriate data products to ensure accuracy and user trust in AI-ready data.
  • AI-ready data architecture:  For organization-wide AI adoption, a flexible, quickly-adaptable data architecture is essential. Utilizing sandboxes for PoC testing, tools like vector databases, and their ongoing management are vital.
Is Generative AI beginning to deliver on its promise in India? AIdea of India update

Know more

Preparing for an agentic future

 

As India prepares for an ‘agentic future’ where AI systems grow increasingly autonomous, the quality, quantity and accessibility of datasets will be key to defining the nation's AI success. Within enterprises as well, the future may see AI entities fulfilling roles like that of Chief Data Officer.

 

Organizations must rethink how data is stored, processed and leveraged for intelligent decision-making. The focus should be on creating flexible, adaptable data architectures that can support the evolving needs of AI systems while maintaining the highest standards of data quality and governance.

 

At the same time, as the scope of regulations expands to include data tracking, confidentiality and risk monitoring, enterprises need to establish a robust AI data governance framework. Uniform data policies are a step towards adherence to both international and domestic AI regulatory standards.

 

The organizations that will thrive are those that recognize data as their most valuable asset and invest in creating AI data ecosystems. With an emphasis on AI data quality, accessibility and governance, businesses can position themselves at the forefront of the AI revolution, ready to harness its full potential for innovation and growth.

Download the full pdf

Related content

Identifying AI generated content in the digital age: the role of watermarking

Explore the future of AI content and the critical role of digital watermarking in protecting creators' rights and ensuring content authenticity.

Insurance for All: enhancing insurance coverage across India

India's insurance growth: Innovating with micro-agents, AI underwriting, digital platforms, and regulatory support for universal coverage.

Is Generative AI beginning to deliver on its promise in India? AIdea of India update

Stay ahead in the digital race with the latest edition of 'AIdea of India.' See how Generative AI is fulfilling its potential across various sectors in India.

    Summary

    Indian organizations adopting AI must prioritize high-quality, trustworthy data to succeed. As a report by EY named Data 4.0: Making your data AI-ready highlights, many organizations struggle with low data maturity, which hinders AI implementation. To leverage AI effectively, companies need to build an open and trusted data foundation. In addition, in terms of data evolution, Data 4.0 emphasizes AI-powered, cloud-native data architectures. Therefore, it is essential to build a robust data foundation that can support the role of AI in innovation, productivity, and competitive advantage. To enable such a scenario and also be ready for AI's agentic future, organizations must focus on enterprise data strategy, management, and governance.


    About this article