I first learned about the beer and diapers concept sometime in the late 90s. It was my introduction to data mining, and I learned more about it as a graduate student. If you’re not familiar with the concept, here’s a brief recap. The legend goes that an industry consultant, Karen Heath, was working with data from a Midwest retailer. When she discovered a statistically significant correlation between beer sales and diaper sales, she forwarded the information to the retailer’s powers-that-be. The interpretation was that on his way back home from work, a new father stops to pick up diapers as well as a case of beer at the same time. The legend also suggests that when the retailer moved the two products closer together, beer sales increased by a significant percentage. This is known as the association rule.
I started flippantly mentioning beer and diapers 2.0 when discussing AI applications with people far more knowledgeable than me and my colleagues’ grimaces said it all.
As they patiently explained some of the more nuanced aspects of AI applications, it became clear the beer and diapers 2.0 analogy isn’t too far off the mark—it just didn’t go far enough. Specifically, the brute force of data mining and analytics associated with traditional BI become an input to modern AI algorithms. The biggest difference comes from what happens after the associations are discovered: Modern AI algorithms are forward-looking and allow us to glean actionable insights from the associations. In the context of an IT organization, the mountains of data in logs, application databases, ERP systems and other sources can generate interesting insights using traditional data mining approaches. But what do we do with those insights? That’s where the AI component comes in. For example, helpdesk procedures for level 1 support might be automatically updated based on AI learnings from a large historical reading of closed tickets. This would reduce the cost of manually updating those procedures, makes front-line staff more productive, and provides actions.
One reason I find Broadcom’s innovative Automation.AI really intriguing is how it can connect beyond just AIOps into other applications to find previously unknown relationships and then recommend actions. Currently introduced in their AIOps platform, Automation.AI is the foundation of the new push toward BizOps in Broadcom’s tool stack. Assuming it realizes its potential, Automation.AI is a game changer that will allow organizations to predict and automatically address specific challenges traditionally requiring human intervention. As Automation.AI continues to expand across Broadcom’s ESD platform, the insights it provides to businesses and IT organizations will be another significant tool for being more responsive to business needs. I’m looking forward to seeing how we can leverage those capabilities across Broadcom’s ESD tools and third-party apps to improve value for Enterprise Studio customers and help them achieve BizOps capabilities in their organizations.
I’m no AI expert, but I’m surrounded by smart people with PhDs, master’s degrees, and others who have an extreme interest in the field. As I do research in an attempt to wrap my brain around the concept of AI, I keep coming back to the beer and diapers model—actually, beer and diapers 2.0.
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