Evolving your data strategy in the era of AI

In the era of AI advancement, data has emerged as a vital asset for making informed decisions, providing guidance, and building trust. Despite recognizing its power, companies struggle to unleash the full potential of data. The primary issue lies in striking a balance between deriving immediate value from data investments and setting the stage for future growth. According to McKinsey, organizations typically employ either a grassroots or big-bang data strategy—neither of which enables them to make the most of their data investments. This challenge is further compounded by the unpredictable evolution of data technologies, the emergence of new data types, and the continuous surge in data volume.

While some Tech Leader & Chief Data Officers (CDOs) have successfully driven innovation through data, challenges like data silos and quality issues remain significant pitfalls. A modern data strategy breaks down silos, empowering teams to conduct analytics and machine learning with proper security and data governance controls. Building this effective data strategy involves creating a scalable infrastructure, instilling a data-driven mindset, and addressing security challenges associated with data in silos.