By: Joshua Finley
The rise of generative AI (GenAI) is reshaping the landscape of the data industry, fundamentally altering how organizations utilize their corporate data. In an interview with Shruti Worlikar, Data Specialist Solutions Architecture leader at AWS, she emphasized that “the shift is towards harnessing both structured and unstructured data stored across transactional databases, data warehouses, and data lakes to feed into GenAI models.” This transformation allows organizations to turn previously dormant information into actionable insights. By integrating GenAI applications, companies can increase workforce productivity, automate tasks, deliver predictive outcomes, and foster innovation, making the most of their data assets.
Needless to say GenAI is a data-hungry endeavor, but it’s not just any data. For enterprises to deliver successful GenAI outcomes, they need high-quality, reliable domain data. They need data that is spread across their data landscape in different types of data stores and data formats. And they need this data to be compliant with their industry and government regulations.
This technological shift has spurred significant changes in data management practices. Worlikar noted that “GenAI has increased the need for more efficient data integration, processing, and management workflows.” To address this, organizations are adopting new tools, strategies and also leaning heavily on their existing tooling landscape that scales to meet their GenAI outcomes. Technologies like AWS Glue, which facilitates data extraction, transformation, and loading (ETL), and Amazon Redshift ML, which allows organizations to run machine learning models directly within their data warehouses, are becoming essential. Worlikar explained that many organizations are now “centralizing their data infrastructure on the cloud for seamless GenAI application integration due to the innovative nature of the cloud that allows a near-seamless transition between the conventional and the new way of delivering business value.”
However, as GenAI becomes more prevalent, ethical concerns surrounding data privacy and security are emerging. Worlikar pointed out that “incorporating corporate data into GenAI raises concerns about data quality, security, privacy, and compliance, especially when sensitive or regulated data is involved.” Organizations must ensure that the data used in GenAI applications is de-identified, encrypted, and compliant with privacy regulations like GDPR and CCPA. To tackle these challenges, companies are implementing privacy-preserving technologies and role-based access controls. “The challenge,” Worlikar stated, “is balancing innovation with strict adherence to data governance frameworks.”
For GenAI, access control and data quality are key ingredients of a data governance framework. And these do not happen in a vacuum. They require a careful curation of cataloging domain data applied with stringent data quality rules, before they are converted in to data products and made available for discovery. Subsequently, these data products are then consumed based on fine grained access controls. Going from raw data to fine data products is another data industry trend that has accelerated with the advent of GenAI.
As the industry evolves, solution providers are also leaning towards a convergence of AI/ML and analytics tools. Worlikar highlighted that “companies are embedding AI capabilities directly within their data warehouses, facilitating automation and low-code/no-code development of data pipelines and data transformation.” Additionally, the emergence of “lake houses” that store both structured and unstructured data will facilitate easier access to datasets for GenAI training and applications. This will improve business decision-making and provide more predictive power to consumers.
Organizations leveraging GenAI can gain a competitive edge by optimizing operational efficiencies. “For instance,” Worlikar noted, “automating report generation from large datasets or creating intelligent chatbots can streamline processes.” A standout success story is Exscientia, which leveraged GenAI to significantly accelerate drug discovery development and achieve substantial cost reductions. “Such examples illustrate how integrating GenAI into marketing analytics can yield personalized customer engagement strategies drawn from real-time insights,” she added.
To stay relevant in this evolving landscape, data professionals must develop new skills. Worlikar emphasized that “skills in integrating corporate data into AI models will be critical,” including proficiency in emerging data stores like vector databases, ETL processes, cloud-based data services, and familiarity with GenAI approaches such as RAG (Retrieval Augmented Generation) and GenAI frameworks like TensorFlow and PyTorch. A solid understanding of data ethics, compliance, and privacy regulations will also be crucial as professionals navigate the complexities of working with sensitive corporate data.
For consumers, the growing influence of GenAI means more personalized and intelligent experiences. Worlikar remarked that “corporate data from databases and warehouses is being used to create recommendation engines, automate customer service interactions, and tailor products to individual preferences.” This shift enables consumers to enjoy more intuitive and efficient services across various sectors, from e-commerce to banking and healthcare.
As organizations unlock the potential of their vast data reserves through GenAI, they can streamline operations, innovate products, and enhance customer experiences—all while addressing the challenges of data privacy, security, and regulatory compliance. The data industry is on the cusp of a transformation driven by the integration of generative AI, and its implications are profound for businesses and consumers alike.
Published by: Annie P.
