Posted By: James Taylor | Posted On: 24th January 2017 |
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 […]
Posted By: Meri Gruber | Posted On: 17th January 2017 |
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 […]
Posted By: James Taylor | Posted On: 16th January 2017 |
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 […]
Posted By: James Taylor | Posted On: 15th December 2016 |
“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:
Posted By: James Taylor | Posted On: 18th October 2016 |
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.
Posted By: Meri Gruber | Posted On: 22nd September 2016 |
Analytic teams are using decision modeling to frame the problem with their business partners, assess the predictive model’s value in business terms and clarify the people, process and technologies needed for successful deployment.
Posted By: James Taylor | Posted On: 2nd August 2016 |
Decision modeling and the graphical decision requirements diagram (DRD)helps analytics teams assess the predictive model’s value in business terms, select the right delivery option and know how to make the analytic actionable.
Posted By: James Taylor | Posted On: 26th July 2016 |
A decision requirements diagram (DRD), developed with with the Decision Model and Notation (DMN) standard, provides analytics projects with focus and direction, delivering value quickly without huge upfront costs or time investment.
Posted By: James Taylor | Posted On: 19th July 2016 |
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.