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 […]
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 […]
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 […]
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: