Business of Tech: Daily 10-Minute IT Services Insights

AI and Data Management: Why Speed Matters More Than Scale in Today's Business Landscape with Robert Buda

Aug 15, 2025

In this insightful discussion, Robert Buda, founder and president of Buda Consulting, shares his expertise in data management and security. He argues that speed is now more critical than scale as businesses adapt to AI. Buda warns against the misconception that simply adding more data will yield better outcomes and highlights the importance of operational data for business continuity. He also addresses the risks of data loss in an increasingly application-driven landscape, emphasizing the need for rigorous data quality practices.

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INSIGHT

Fundamentals Persist As Speed Increases

Data management fundamentals remain unchanged even with AI's arrival.
The real shift is the speed required to respond to new technologies and applications.

INSIGHT

Application Velocity Outweighs Pure Scale

The explosion of new applications forces data teams to react much faster.
Speed of integration and access control matters more today than merely handling large volumes.

INSIGHT

Big Data Collection Was Lopsided

Many organizations collected huge amounts of big data but rarely used most of it.
That big-data wave largely underdelivered, but AI promises more practical usage of stored data.

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Dave Sobel interviews Robert Buda, founder and president of Buda Consulting, about the critical role of data management in the age of AI. Buda emphasizes that while the principles of data management remain unchanged, the speed at which organizations must adapt to new technologies has increased dramatically. He argues that ensuring data quality is more crucial than ever, especially as organizations begin to rely on AI models for decision-making. Buda expresses concern that some in the AI community believe that simply feeding more data into models will lead to accurate outcomes, a notion he finds misleading.

The conversation delves into the tension between speed and scale in data management. Buda explains that the rapid influx of new applications requires data professionals to react quickly, making speed a more pressing issue than the sheer volume of data. He acknowledges that while big data was heavily discussed, much of it went unused, contrasting this with the current AI landscape, which he believes will have a more significant impact on how data is utilized.

Buda also challenges the perception that organizations overestimate the value of their data. He argues that many companies underestimate the criticality of their operational data, which is essential for their functioning. He highlights the risks associated with data loss, such as ransomware attacks, and stresses that all data, not just sensitive information, is vital for business continuity.

As the discussion wraps up, Buda emphasizes the importance of maintaining rigorous data accuracy and proper metadata management. He warns against complacency as organizations adopt AI technologies, advocating for a proactive approach to ensure that data is well-structured and labeled. This diligence will be essential as businesses increasingly rely on AI-driven insights for operational decisions, making the quality of data more important than ever.

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