decision modeling

Update to the Decision Management Systems Platform Technologies Report

The Decision Management Systems Platform Technologies Report began in early 2012 as a way to share our research and experience in building Decision Management Systems. Since then we have extended, updated and revised the report many times. This week we released the latest version – Version 8 – with a new, easier to use format. There is […]

From BI to Predictive Analytics with Decision Centric Dashboards

Many organizations are keen to improve data-driven decision making with predictive analytics but they are trapped by operational demands for traditional BI reporting and dashboards. They are asking how do they get “there” – better data-driven decisions – from “here” – traditional BI reporting and dashboards. This challenge is readily addressed with decision modeling and modern […]

Great Examples of Integrating BI and Data Science

We talk a lot about the power of predictive analytics*. Data-driven decision making is the goal, but to get there, organizations need to learn how to extract actionable information from their data. We also talk a lot about how this is different from traditional business intelligence (BI), where the focus is on historical reporting. But […]

Global Big Data Conference: Don’t Apply Big Data Analytics To The Wrong Problem: Put Decisions First

I am speaking at the Global Big Data Conference in Santa Clara, CA in March on Don’t Apply Big Data Analytics To The Wrong Problem: Put Decisions First One of the biggest challenges for analytics Teams is effective communication with their business partners. Too many projects fail to connect problems in the business environment to […]

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 […]

IBM InterConnect presentation: Pioneering Clinical Decision Services with Decision Modeling at Kaiser Permanente

Kaiser Permanente is developing clinical decision support (CDS) applications that automate real-time decision services using IBM ODM. Well-designed decision services can replace existing guideline documents in all areas of its business. Guidelines are expensive to develop as they involve significant investment from SMEs. Even good guidelines are of limited use for real-time decision-making, especially if […]

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 […]

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: