Data Mining

Delivering the Business Value of Analytics. Free Webinar

Learn three critical success factors in delivering business value from your advanced analytics investments. Free Webinar August 14, 2018, 1:00 PM ET / 10:00 AM PT Register here. 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 […]

Aragon Research: Decision Management Platforms

Jim Sinur of Aragon Research just released a new report: Decision Management Platforms Sharpen Decision Outcomes for Enterprises An organization’s ability to execute superior decisions will be a huge differentiator in today’s ever-changing business environment. Currently, enterprises are faced with the challenge of making faster decisions, despite the limited skill base to assist more frequent […]

Predictive Analytics World 2018

This year Predictive Analytics World is coming to Las Vegas June 3-7, 2018, for the largest Predictive Analytics World event ever. There’s a packed agenda including a great Analytics operationalization and management track focused on how to take analytics (and AI) and get real business value from them. The track kicks of with James Taylor, Decision […]

Decision Modeling and CRISP-DM for Modern Data Science Projects

Many data science projects use the popular and well established CRISP-DM methodology. However, CRISP-DM has limitations especially regarding business understanding and deployment. The decision modeling process and the graphical decision requirements diagram addresses these challenges. CRISP-DM Popular, but with Limitations Gregory Piatetsky of KDnuggets writes following the KDnuggets Data Mining Methodology Poll: “CRISP-DM remains the […]

Great New Case Study – Bringing Clarity to Data Science and Analytic Projects

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 […]

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.