predictive analytics

Job Opening at Decision Management Solutions – Delivery Management

Decision Management Solutions is growing and looking for a Delivery Manager for its projects. The Delivery Manager will be an experienced hybrid agile project manager and will be responsible for managing several concurrent, discipline based, high visibility projects using agile, and fixed milestone methods in a fast-paced environment that may cross multiple internal business divisions […]

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 […]

New White Paper on Operationalizing AI with Digital Decisioning

We recently published a new white paper Operationalizing AI: Beyond AI Pilots with Digital Decisioning. Businesses are made or broken by the quality of the decisions they make. How well does this offer target this customer? Does this machine need to be serviced now or can it wait? Is this transaction legitimate or suspicious? Will […]

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 […]

Claim Complexity

FREE Online Decision Requirements Modeling Training

If you’re stuck sheltering in place or bored working from home, we have a great opportunity for you. You can learn a vital new skill – Decision Requirements Modeling – from the comfort of your home FOR FREE! This has been completed. Please contact us if you are interested in decision requirements modeling. We are […]

Getting The Claims Outcomes You Want With ML/AI and Digital Decisioning

Neal Silbert of DataRobot had an interesting post on the DataRobot blog last week – Outcome-Based Claims Assignment: The “New Grail” of Insurance. He talks about using ML and AI to improve the assignment of claims adjusters to commercial claims. As he says “why make exceptions [to ensure the best assignment] when you can make […]

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 […]

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.