Unifying Data Strategy Key to Transforming AI Experiments into Revenue Generators, Says Snowflake’s Martin Frederik

The success of AI initiatives within companies is increasingly dependent on the quality of their data, with many promising projects stalling before reaching production. To transform these experiments into revenue generators, a clear focus on data strategy is crucial, as Martin Frederik, regional leader for Snowflake in the Netherlands, Belgium, and Luxembourg, emphasizes.

“AI without a solid data strategy is like a car without fuel,” Frederik says, highlighting that AI applications, agents, and models are only as effective as the data they’re built upon. Without a unified, well-governed data infrastructure, even the most advanced models can fall short, he adds.

This predicament often arises due to leadership focusing on technology rather than business needs, Frederik notes. “AI is not the destination; it’s the means to achieving your business goals,” he advises. When AI projects encounter obstacles, they are typically misaligned with the business, teams lack communication, or data quality suffers.

However, statistics suggesting that 80% of AI projects never reach production should not be discouraging, according to Frederik. Instead, he posits that it represents a part of the maturation process. For those who get their foundational data strategy right, the rewards are substantial: a recent Snowflake study revealed that 92% of companies are already experiencing returns on their AI investments, with every £1 spent generating £1.41 in cost savings and new revenue.

To achieve this level of success, Frederik emphasizes the importance of a “secure, governed, and centralized platform” for data from the outset. A company culture that fosters widespread access to quality data and AI tools is also essential to drive AI adoption at scale.

Breaking down departmental barriers and ensuring everyone has access to high-quality data and AI resources are key steps towards achieving this goal. By doing so, AI becomes a shared resource rather than a siloed tool, enabling teams to work more collaboratively and make faster, smarter decisions together.

The emergence of AI agents that can understand and reason over various types of data is a significant advancement, particularly as unstructured data makes up 80-90% of a typical company’s data. These new tools enable staff with varying technical skill levels to ask complex questions in plain English and receive answers directly from the data.

Frederik refers to this development as “goal-directed autonomy,” where AI agents can autonomously determine necessary steps, such as writing code and gathering information from other apps, to deliver complete answers. This automation will significantly reduce the time-consuming aspects of a data scientist’s job, freeing up their expertise for higher-value tasks.

Snowflake is a key sponsor of this year’s AI & Big Data Expo Europe and will have representatives sharing insights on making enterprise AI easy, efficient, and trusted at stand number 50 during the event. For more information on upcoming enterprise technology events and webinars, visit TechForge Media.