International Institute for Analytics

Transform.AI brought together enterprise and startup CEOs, economists, technologists, venture capitalists, and journalists. We discussed the myths and realities of the economic impact of AI, enterprise applications of AI, the ethical questions surrounding AI, and the state of what’s possible in the field. Here is part three of the highlights.

Inquiry:

Our analytics team is doing more analysis in the predictive space. What is the value of this work and how do we know we’re picking projects that will have an impact on the business?

There seems to be some confusion as to exactly what artificial intelligence (AI) is, and how the discipline of AI should be categorized. Is AI a form of analytics or is it a totally new discipline that is distinct from analytics? I firmly believe that AI is more closely related to predictive analytics and data science than to any other discipline. One might even argue that AI is the next generation of predictive analytics. Additionally, AI is often utilized in situations where it is necessary to operationalize the analytics process. So, in that sense, AI is also often pushing the envelope of prescriptive, operationalized analytics. It would be a mistake to say that AI is not a form of analytics.

Transform.AI brought together enterprise and startup CEOs, economists, technologists, venture capitalists, and journalists. We discussed the myths and realities of the economic impact of AI, enterprise applications of AI, the ethical questions surrounding AI, and the state of what’s possible in the field. Here is part two of the highlights.

Inquiry:

We have an external reporting tool with a certified process and strong governance. Separately, we have management reporting data and analytics data, and for analytics we are standing up a data lake from disparate sources. My challenge is around the external tool and management reporting data. Right now we bridge the management reporting back to the externally reported figures, but I don’t see a need for this tie because the purposes are different. However, senior management has an emotional commitment to the bridge, so I’m wondering if that bridge is really important and whether you are seeing companies diverting from the need to build a bridge.

Transform.AI brought together enterprise and startup CEOs, economists, technologists, venture capitalists, and journalists. We discussed the myths and realities of the economic impact of AI, enterprise applications of AI, the ethical questions surrounding AI, and the state of what’s possible in the field. Here are some highlights.

Sarmila Basu’s team of data scientists is using machine learning and modeling to save Microsoft millions of dollars on heating, cooling, and other facilities maintenance costs.

Inquiry:

I’m interested to discuss a topic that I can best describe as ETL (extract, transform, load) as a service that can be deployed for the enterprise. I’m looking to see if other companies have successfully attempted this, and whether there are vendor platforms for this or if this is typically a capability that is built internally.

“The real issue is that every business is now a tech business – whether it wants to be or not – and that means not just new skills and experiences, but a new outlook on opportunity and strategy.” This quote is from a recent Forbes analysis of the different business models companies can deploy and how each one is performing in the new digital world.

Inquiry:

Our analytics department is starting a knowledge management program. What are some best practices of starting a program for our analytics department? We’re looking for lessons learned and anything that would help a knowledge management program be more successful for people doing analytics.