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 […]
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 […]
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 […]
A recent McKinsey paper titled Jump-starting resilient and reimagined operations had some great advice for operational executives working on re-starting and reimagining their operations for a COVID-19 world. In particular, it identified 5 themes that will share post-COVID-19 recovery efforts in operations: Building operations resilience Accelerating end-to-end digitization Rapidly increasing expense transparency Driving the future […]
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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 […]
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 […]
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 […]
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.
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 […]