Machine Learning

Driving Business Value from Advanced Analytics, Machine Learning and AI: New Webinar on Digital Decisioning

The International Institute for Analytics (I’m a faculty member) recently hosted me for a webinar on Digital Decisioning: Driving Business Value from Advanced Analytics, Machine Learning and AI. In the webinar I discussed the opportunities and challenges of Machine Learning (ML) and Artificial Intelligence (AI), introduced Digital Decisioning – it’s value, principles and how to […]

Fraud, AI and Digital Decisioning

Automating decisions about transactions lets them be handled in real-time, providing better customer service. If that decision fails to detect fraudulent transactions, this lets fraud into the system. Once a fraudulent transaction has been allowed, you are committed to a “pay and chase” approach to eliminating it. This has a poor success rate. Much better […]

Three Steps to Boost Your Deployment Score

As we approach the end of the year, our friends over at the International Institute for Analytics (IIA) are re-tweeting some of their best content. One post in particular caught our eye – What’s Your Deployment Score? This is a great piece by Tom Davenport (who wrote a foreword for my new book Digital Decisioning: […]

Machine Learning, Trust and Stephen Covey

Trust is a big deal when it comes to machine learning. “Black box” algorithms, concerns about bias and a sense that data scientists may know everything about the data but nothing about the business all undermine trust in machine learning models. Indeed, building machine learning models that can be, will be, trusted is regarded as […]

Is Your AI Project About Customer Experience or About Business Decisions?

Many organizations are investing in artificial intelligence (AI) initiatives these days. However, many are also lumping all of the uses of AI into the same program. This is a terrible idea because AI technologies are being used for two very different business outcomes. AI success relies on a clear focus on business outcomes rather than […]

What is an Acceptable Analytic Failure?

Many speakers on predictive analytics, machine learning (ML) and AI talk about the need to allow data science teams to fail. Without failure, without a willingness to fail sometimes, it’s very hard to build a successful data science program. This is true and often a barrier for companies that find it hard to accept that not all analytics initiatives succeed.