For years, companies have received strong recommendations to develop a comprehensive and forward-looking data strategy. However, just as businesses were making strides in this direction, the rapid advancements in AI pose a potential challenge to render last year’s plans obsolete.
Fortunately, our experts unanimously agree that if you have already invested effort in establishing a robust data strategy, you are on the right path. Jennifer Belissent, Principal Data Strategist at Snowflake, emphasizes that the generative AI era does not demand a fundamental shift in data strategy but rather an acceleration of the trend towards breaking down silos and opening access to data sources within the organization.
Mona Attariyan, Snowflake’s Director of Machine Learning, underscores the importance of the level of your data strategy and the urgency to execute and invest in it promptly. Falling behind in acceleration could risk being left behind virtually overnight.
However, embracing generative AI doesn’t imply pursuing all promised miracles simultaneously. While assistance with basic coding and copywriting is beneficial, leaders should prioritize unique insights derived from their own data. According to Snowflake Co-Founder Benoit Dageville, applications of generative AI will touch various aspects of a business, but focusing on core needs remains paramount.
Snowflake CIO Sunny Bedi stresses that governance is non-negotiable and a prerequisite for entering the world of generative AI and LLMs. Security, governance, and compliance are deemed the minimum requirements.
In addressing the challenges posed by the new AI era to the IT status quo, Bedi and Anoosh Saboori highlight the significance of data placement. Generative AI’s influence will drive a trend towards centralized data on a managed service platform, providing the necessary security and governance while creating a single source of truth for LLMs and other applications.
Saboori, Snowflake’s Head of Product Security, foresees a rapid shift for cloud-adoption laggards, as generative AI makes it challenging to maintain on-premises data. The move to the cloud, while inevitable, presents complexities in security and compliance strategy, especially with the adoption of multi-cloud approaches leading to distributed data and models across platforms with varying security and governance capabilities.