Data-Centric AI: A New Approach to Making Business Decisions
Introduction: The Shift from Model-Centric to Data-Centric AI
What if the key to unlocking AI’s full potential isn’t hidden in more complex algorithms, but in the one commodity that’s often overlooked–data? Since the seminal 1956 Dartmouth conference, AI has evolved from rule-based expert systems to deep learning. Yet, as we approach the next era of AI, it’s clear that the future lies in rethinking our approach to data.
For years, most discussions surrounding AI have focused on developing more advanced models. This model-centric approach has yielded impressive results, from image recognition systems that outperform humans to large language models (LLMs) and small language models (SLMs) capable of generating human-like text. Yet, as organizations work to implement AI at scale, they’re encountering limitations that no amount of model tweaking can overcome. The root cause? The quality and relevance of the data feeding these models.
While impactful, the model-centric approach often treats data as a static input, focusing innovation efforts on algorithmic improvements. This approach has led to diminishing returns, with marginal gains in performance coming at the cost of exponentially increasing model complexity. Additionally, it has created a disconnect…
