Our experience with decision modeling with the Decision Model and Notation (DMN) standard is broad and deep. We have trained nearly 1,000 people and we have used decision modeling on dozens of real-world projects. Decision modeling is the cornerstone of decision automation projects like Fraud Detection, Claims Handling and Customer Next Best Action. Decision models […]
Silicon Valley research firm cites Decision Management Solutions as a visual and business-friendly extension to digital business platforms Palo Alto, CA – June 22, 2017 Leading Decision Management consulting and decision modeling software provider, Decision Management Solutions, today announced that the company has been named a 2017 Hot Vendor in Digital Business Platforms by Aragon […]
We have been helping several organizations improve their analytic and data science projects. Like many users of analytics, these organizations find that their analytic teams often lack a clear understanding of the business problem, resulting in projects that lose their way or produce analytic models that don’t get operationalized, deployed or used. We have helped […]
The biggest challenge facing organizations adopting analytics is closing the gap between business value and analytics results. This is becoming increasingly serious as more organizations make investments in data mining, predictive analytics, data science, machine learning and all forms of analytics. Ensuring that these investments in analytics and analytic technology show a return means understanding how […]
One of my LinkedIn contacts recently pointed to this great little article on HBR – How to Tell If Machine Learning Can Solve Your Business Problem – and it makes some points that show the potential for decision modeling to help you better apply machine learning and other analytic techniques. The author begins by pointing out that automation is […]
“Making predictions is hard, especially about the future” is a well known witticism. When it comes to making predictions about how companies will make predictions, it can be even harder to know what to say. Nevertheless, the folks over at KDnuggets recently asked some of the leading experts in Data Science and Predictive Analytics for some thoughts on developments in 2016 and trends for 2017. I was one of those that participated and you can see the article here – Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017. Several key themes emerged from the various expert responses:
One of my big focus areas right now is using decision requirements models to frame predictive analytic projects. We are working with a few insurance companies as well as a couple of manufacturing companies to apply decision requirements modeling in their analytic requirements process and I am excited by the potential. I recently wrote an […]
A big focus area right now for organizations looking to adopt or expand operational predictive analytics is using decision requirements models to frame requirements for their predictive analytic projects. We are working with a few insurance companies as well as a couple of manufacturing companies to apply decision requirements modeling in their analytic requirements process and I […]
James live blogged from this week’s IBM Watson Analytics event in New York earlier this week. Don’t miss the series posted to www.jtonedm.com. Table of contents for IBM Watson Analytics Cloud IBM Watson Analytics Cloud Announcement KickOff IBM Watson Analytics Cloud Customer Panel IBM Watson Analytics Cloud Business Partner Panel IBM Watson Analytics Cloud – […]
Marie Wieck came back to discuss how to create the kind of composable business IBM discussed on day 1. It’s critical, she says, to keep customers at the center and drive a new process from this. It’s key to use data to make better decisions, and it’s important to have the kind of scalable infrastructure you need to make this scale and deploy effectively.
Once again the focus is on IBM customers and HH Gregg came on stage to talk about their journey. As a regional retailer they need to need to differentiate themselves. In particular they have focused on providing a “friction free” shopping experience on every channel. They have historically invested in training for their staff and have tried to bring this kind of personalized service to their mobile strategy. They determined that it was not enough to “mobilize” their existing interface, it had to reflect the different form factor and interaction patterns. Mobile had to lead not jus