Posted on

Stop creating self-fulfilling prophecies: How to apply AI to small data problems



Share

Amnon Mishor
Contributor

Amnon Mishor is the CTO and founder of Leadspace, an industry-recognized, AI-powered buyer data platform used by B2B companies like Zoom and Salesforce.

Over the past decade or so, the digital revolution has given us a surplus of data. This is exciting for a number of reasons, but mostly in terms of how AI will be able to further revolutionize the enterprise.
However, in the world of B2B — the industry I’m deeply involved in — we are still experiencing a shortage of data, largely because the number of transactions is vastly lower compared to B2C. So, in order for AI to deliver on its promise of revolutionizing the enterprise, it must be able to solve these small data problems as well. Thankfully, it can.
The problem is that many data scientists turn to bad practices, creating self-fulfilling prophecies, which reduces the effectiveness of AI in small data scenarios — and ultimately hinders AI’s influence in advancing the enterprise.

The trick to applying AI correctly to small data problems is in following correct data science practices and avoiding bad ones.

The term “self-fulfilling prophecy” is used in psychology, investing and elsewhere, but in th …

Read More