predictive analytics

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.

This Week at Think 2019: Delivering Excellent Customer Experiences with Analytics and Automation

Think 2019 is here! Sharpen your skills. Get hands-on experience with the latest technology. Extend your professional network. It’s virtually impossible not to learn something new among this celebrated community of technologists and thought leaders. And have some fun while you’re at it. Explore the technologies that are redefining industries, learn from the experts, and get […]

International Institute for Analytics 2019 Predictions – Some Thoughts

Several of this year’s International Institute for Analytics predictions point towards a DecisionsFirst decision management approach.

DecisionsFirst Analytics in Forbes

Joe Decosmo, CAO of Enova Decisions and someone we know from our work at the International Institute for Analytics wrote a nice piece in Forbes today – Why You Should Take A Decision-First Approach To Analytics. Joe makes some great points in his post and leads off with a shout out to the Decision Management Manifesto. Like […]

Don’t bite off more AI than you can trust

Many, most, companies are actively considering using AI in their business, but few are making much progress. Some of this is due to a poorly defined approach. Some is due to confusion as to what AI means – mixing conversational AI (a user experience technology) with decision-making AI (an extension of predictive analytics and machine […]

Personalizing Insurance Marketing with Decision Management

A key way for insurers to respond to disruption in the insurance market is to more effectively personalize their marketing. Targeting prospects and especially customers with unique, personalized messages is a way for traditional carriers to be “disruptive”. Indeed there are those who see personalization—reaching customers with targeted messaging, offers, and pricing at just the […]

Delivering the Business Value of Analytics

Learn three critical success factors in delivering business value from your advanced analytics investments. Many organizations still struggle to get a business return on their investment on advanced analytics. The biggest barrier? An inability to integrate analytics, especially predictive analytics, into frontline systems and business processes. Work with a number of global companies has revealed […]

Adopt Decision Modeling for DecisionsFirst Analytic Success

Add decision modeling and a DecisionsFirst approach to analytics to this established AI Solution Decomposition Process to make it more effective.

Don’t Use Executable Decision Models – Part 4

This is the final part in my series on why we don’t recommend using executable decision models. Part 1 discussed the problem of additional model technical complexity required for executable models, resulting in business users disengagement. Part 2 detailed the challenges of reuse and maintenance with an executable model, and Part 3 outlined how executable […]

Don’t Use Executable Decision Models – Part 3

This is the third of four posts in my series on why not to use executable decision models. Part 1 discussed the difficulty in sustaining business user engagement when using executable models, and part 2 outlined the challenges of reuse and maintenance with executable models. Reason #3: Analytics and AI We are using decision models to […]