by Charlotte DeKeyrel, Expert Decision Modeler Decision Management Solutions You know who they are—the members of the brain trust in your organizations. They are the seasoned subject matter experts who understand business decision-making and business processes down to...
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...
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...
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...
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...
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...
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...
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...
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.
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...
80% of data and analytics leaders with global life insurance and property & casualty carriers surveyed by McKinsey reported that their analytics investments are not delivering high impact. What’s stopping them from delivering high impact? 38% of those surveyed...
I am often asked about the difference between decision support systems and decision management systems for decision automation. All decisions involve a choice, a selection of a course of action from a set of alternatives and generally result in an action being taken,...
Get out of the reporting quagmire, be explicit about decision making with decision modeling and integrate analytics in BI and operational systems. We're Not There Yet "The activities of analytics teams and the investments made to support them aren't in sync with what...
Many organizations are keen to improve data-driven decision making with predictive analytics but they are trapped by operational demands for traditional BI reporting and dashboards. They are asking how do they get “there” - better data-driven decisions - from “here” -...
We talk a lot about the power of predictive analytics*. Data-driven decision making is the goal, but to get there, organizations need to learn how to extract actionable information from their data. We also talk a lot about how this is different from traditional...