Predictive Analytics

Predictive Analytics World 2017: The Role of Decision Modeling in Creating Data Science Excellence

Join me and Tina Owenmark of Cisco when we speak on The Role of Decision Modeling in Creating Data Science Excellence at Predictive Analytics World in San Francisco. Cisco’s Data Science Office focuses not just on data science, but also on shaping the questions and answers for Cisco’s operational groups. They focus not on technology or […]

Decision Modeling Brings Clarity to Analytics – New Podcast

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 […]

Can Machine Learning Solve Your Business Problem?

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 […]

Some Analytic and Data Science Predictions 

“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:

Analytics Teams: Before You Deploy

As I discussed in my earlier post, analytics or data science teams know that two key challenges for analytics projects are making sure you solve the real business problem (framing the problem) and making sure you can operationalize the result (deployment).  In this second post I am going to talk about deployment.

How To Fix The Broken Links In The Analytics Value Chain

Here we see the classic challenge of analytics – 70% think it is very or extremely important but only 2% say their analytics efforts have a broad, positive impact. And like every other assessment of this problem, including ones we have conducted here at Decision Management Solutions, the problem is not in the data or in the analytics themselves! The problems are in framing the problem and operationalizing the results. Everyone seems to have the analytic technology they need but they just aren’t getting it to work for them.