Decision Management

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 […]

James’ Notes from the Field

I’m in Asia for two weeks. Last week I had the privilege of catching up with marketing and operations leaders at a major local insurer. We built both a next best offer (NBO) solution and a claims handling solution for them. Both teams are self-sufficient, with multiple business people making changes to the decisioning system. […]

Predictive Analytics World 2019 – What I Learned and What I Said

I presented on Backwards Engineering – planning Machine Learning (ML) deployment in reverse. Data shows that a traditional data-first approach to analytics is not generating much value for companies and I urged the audience instead to adopt a decisions-first mindset. The last mile – getting ML models embedded into production systems – is critically important […]

Algorithms and Regulations: Tips for Success

Cathy O’Neill wrote an interesting piece on regulating automated decision making recently. I am not going to argue about whether use of algorithms should or should not be regulated because I think it is inevitable that they will be. The question is how companies should respond to these regulatory efforts as they are rolled out […]

Using Technology to Reduce Operating Costs in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance”. That post, referenced Matt Josefowticz’s article – Technology May be the Answer for Insurers, but What Was the Question?, in which he states that there are only three levers of value in insurance: 1. Sell More, 2. Manage Risk Better, and 3. Cost Less […]

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 […]

Gartner Analyst’s Take on Decision Management: What It Is and Why You Need It

February 20th, 2019 W. Roy Schulte, Research Vice President and Distinguished Analyst at Gartner gave an interesting webinar titled, “Decision Management: What It Is and Why You Need It.”  Roy kicked off the webinar by stating that “companies make millions of operational decisions every day,” and how well (or not) they make these decisions is […]

From Machine Learning to Business Learning

Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. — Wikipedia Machine Learning is increasingly widely used to make predictions. Using machine learning makes it easier and […]