By Murad Wagh, Director, Sales Engineering for India, Snowflake
As Indian businesses navigate a rapidly evolving technological landscape, there is a critical need for agility and innovation across various industries. By 2027, India is on its path to becoming the third-largest economy with a GDP of $5 trillion, and data and AI can play a pivotal role, with government initiatives like the India AI Mission being at the forefront of driving industry-wide awareness. It is particularly true in the context of Generative AI (Gen AI), which transforms work, accelerates innovation, and reinvents business processes. Advancements in language-based AI are ushering in a new era of enterprise transformation, prompting organizations to ramp up investments in Data and AI-related projects.
However, unlocking the true power of Gen AI hinges on a critical element: data. The volume, velocity, and complexity of data and data types will continue to increase. And it’s not only any data – IDC, for instance, estimates that up to 90% of data is unstructured video, images, and documents – resources from which enterprises still need to get much value.
The challenge: Bridging the gap between aspiration and reality
Data is the lifeblood of GenAI, and while many businesses recognize the value of real-time data for gaining a competitive edge, many need help managing it effectively and establishing the importance of reliable data pipelines. Organisations need secure systems for sharing data both internally and externally. However, managing data scattered across various cloud and on premises platforms adds complexity.
The advantage lies with companies that utilise modern data platforms. These platforms streamline data management by breaking down data silos, eliminating redundancy, and ensuring data quality. Empowering organisations to gain faster insights, develop innovative applications, and make better decisions.
Unlocking value from proprietary data
Large-scale machine learning (ML) and Gen AI offer the potential to provide unique insights and recommendations, but this depends on organisations harnessing their data. This progression moves beyond generic internet-trained chatbots to generating highly relevant content that integrates current and potentially confidential enterprise data. By exerting control over data, it becomes possible to leverage these technologies for more precise and valuable applications. However, modern enterprises operate globally with diverse operations, products and value chains that decentralize data generation.
Organizations will need to enable direct usage of primary, current data without necessitating redundant copies, all while meeting evolving regulatory standards associated with AI. That requires mechanisms that ensure data can be shared and accessed securely across multiple cloud environments in compliant and regulated settings. Frictionless but trusted third-party access to valuable datasets also introduces fresh opportunities for value creation, supporting strong governance and security while facilitating innovative data utilization across enterprises.
Achieving agility and safety requires a robust data infrastructure with security and governance at its core. Gen AI democratizes access to insights once limited to AI specialists and data scientists, but this simplified access increases security and governance risks. Therefore, organizations must build a strong foundation that instills confidence in all teams about the quality of the data they use, whether it comes from within the enterprise or external sources.
The future
Many organisations have made strides in addressing the initial phase of the data challenge: enabling structured data to be shared across corporate boundaries and with external parties. However, the second phase—establishing trust in the vast influx of unstructured, real-time data—is still a work in progress for most. A data platform that brings computing resources to the data ensures that all operations adhere to the same governance standards. The third aspect is skilling employees and ecosystem partners to accelerate POCs and implement them faster for initial success.
The goal is to expand accessibility to industry-leading AI and LLMs via intuitive experiences and serverless functions. Every user – not just AI specialists – can then access and use these cutting-edge technologies and apply all their trusted data to train and prompt both custom-built and open-source LLMs. Regardless of your organisation’s current stage or aspirations, investing in a modern data platform offers strategic benefits. Organizations can enhance how data is managed and secured by optimizing data pipelines in areas that provide the highest potential value.
Gen AI and ML are rapidly emerging as critical differentiators across various industries. In this context, democratisation plays a crucial role by facilitating the seamless incorporation of AI into everyday analytics, accelerating innovation by allowing technical users to create and implement AI applications rapidly, and ensuring robust security and governance for all data and models utilized.
These measures secure a competitive advantage and pave the way for sustained success in an increasingly AI-driven world.