Predictive Analytics

Machine Learning BACKWARDS – A Conversation with Eric Siegel

Eric Siegel and I had a great discussion about doing Machine Learning BACKWARDS recently – you can watch the recording below or on our YouTube Channel. Eric, if you don’t know, is the founder of Predictive Analytics World, a leading consultant, and author of “Predictive Analytics“. You can also check out Eric’s new Coursera class. This […]

Decide to Decide Digitally: New Forrester Research

Mike Gualtieri and Boris Evelson of Forrester recently published a great new paper Introducing AI-Powered, Human-Controlled Digital Decisioning Platforms (subscription or payment required) and you should get access to this paper and read it now. It’s got some great content and recommendations and is well worth your time and money.  It follows on from previous […]

Three Critical Success Factors in Building an Analytic Enterprise

Everyone it seems wants to be an analytic enterprise. But what does it mean to be an analytic enterprise? An analytic enterprise applies analytics deeply and broadly. It uses analytics to solve its most critical run-the-business problems. It uses increasingly advanced and analytics to maximize its ability to get value from its data. And it […]

The Purpose of Analytics is not Reporting or Monitoring but Deciding

Some years ago we did some research on the landscape of analytics capabilities. The research looked at the increasingly broad portfolio of analytic capabilities available to enterprises – everything from traditional Business Intelligence (BI) capabilities like reporting and ad-hoc queries to modern visualization and data discovery capabilities as well as advanced (predictive) analytics. One of […]

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 […]

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: […]

Using Technology to Better Manage Risk in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance”. In that post, I referenced Matt Josefowticz’s article – Technology May be the Answer for Insurers, but What Was the Question?, in which he states there are only three levers of value in insurance: Sell More, Manage Risk Better (aka underwriting and adjusting), […]

Using Technology to Grow Relationship Value in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance.” In that post, I referenced Matt Josefowticz’s recent article – Technology May be the Answer for Insurers, but What Was the Question?, in which he argues that there are only three levers of value in insurance: 1. Sell More 2. Manage Risk Better (aka […]

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.

Tips for Successful Data Science Implementation in Insurance

Nancy Casbarro and Deb Zawisa of Novarico recently published a new paper on Data Science in Insurance: Expansion and Key Issues subscription required) that was summarized in this nice little article on Dig-in  3 challenges facing insurers in data science implementation. These three challenges – getting business buy in, attracting talent, and strategic alignment are exactly […]