Chapter 2. Why Data Decides AI’s Success
When it comes to deploying AI in the enterprise, much of the attention is focused on cutting-edge models and flashy applications. They dominate the headlines. But there is something critically important that gets short shrift: data. Even when it’s mentioned, it seems more like an afterthought or something where you check boxes.
Yet, without high-quality data backed up with a solid strategy, any AI implementation is doomed to fail. Data is the foundation of successful AI. It allows for better decision-making, accurate content, and effective searches, just to name a few. A successful AI strategy must be rooted in a deep understanding of data, not just where it’s coming from but how it’s processed, stored, and continuously optimized.
Consider a survey from S&P Global Market Intelligence and WEKA. According to more than 1,500 AI practitioners and leaders, the biggest challenge for AI deployments is storage and data management. By comparison, 26% of the respondents pointed to computing resources such as access to GPUs (graphics processing units), and 23% to security. This survey is not an outlier, either. Various others back up this conclusion.
In this chapter, we will look at the landscape of AI, but with a focus on the importance of data and its new lexicon, which is essential to this conversation.
Understanding the Different Modes of AI
At a conference in 1955 in Dartmouth, Stanford professor John McCarthy coined the term “artificial intelligence” ...
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