Posted By: Meri Gruber | Posted On: 21st January 2016 |
We are pleased to announce a collaboration with LuxMagi on a new book, Real-World Decision Modeling with DMN. The book is being written by leading decision modeling experts James Taylor, CEO of Decision Management Solutions and Jan Purchase, founder of Lux Magi. Read the full announcement here. “A well-defined, well-structured approach to Decision Modeling (using the OMG […]
Posted By: James Taylor | Posted On: 16th April 2015 |
I am speaking at PASS Business Analytics 2015, April 20-22 in Santa Clara CA, on A New Approach to Defining BI Requirements: Most organizations lack an approach that lets them specify their requirements for BI or for analytics more broadly. Their ability to find opportunities for, and successfully use, more advanced analytics is limited. In […]
Posted By: Meri Gruber | Posted On: 19th September 2014 |
The path to a better bottom line is paved by large numbers of operational decisions made by people, by processes and by software applications. Systematically improving each operational decision – at scale – is at the core of Decision Management. Business Architects and Analysts identify, describe and model operational decisions in Decision Discovery. In our […]
Posted By: James Taylor | Posted On: 19th May 2014 |
Live Training as part of Brainstorm San Francisco
Decision Management and business rules allow the effective automation of decision-making combined with increased business agility. Adopting decision management and business rules technology improves customer service, enables 1:1 marketing, increases straight through processing and more effectively leverages scarce resources.
This course delivers a proven and standards-based approach to identifying, understanding and automating business decisions. With examples drawn from a variety of industries and use cases, this course emphasizes the modeling of decisions and the importance of managing both decisions and business rules over the long term. In addition the course shows how effective decision management simplifies and improves business processes, explains how business rules management systems add value and outlines multiple approaches for identifying suitable decisions. Decision management and business rules
Posted By: James Taylor | Posted On: 23rd January 2014 |
To wrap up the series I have been writing on standards in predictive analytics, here’s the report I have been working on. This report discusses each of the topics in the series – R, Hadoop and PMML – in more detail and pulls it all together in a single paper. You can get the Standards in Predictive Analytics paper here.
This 6-session online live training class will prepare you to be immediately effective in using the Decision Management approach and a modern, collaborative and standards-based approach to decision modeling.You will learn how to identify and prioritize the decisions that drive your success, see how to analyze and model these decisions, and understand the role these decisions play in delivering more powerful information systems. This course is newly designated an IIBA Endorsed Course so you will earn 9 PDs/CDUs for attending.
Sessions begin at 10am Pacific/1pm Eastern and are 90 minutes in length. The 6 sessions spread over two weeks:
February 4,5, and 6
February 18,19, and 20
Decision Management, of course, is a proven approach for adopting business rules and pr
Posted By: James Taylor | Posted On: 9th January 2014 |
The third post in my series on standards in Predictive Analytics is on R, a hot topic in analytic circles these days. R is fundamentally an interpreted language for statistical computing and for the graphical display of results associated with these statistics. Highly extensible, it is available as free and open source software. The core environment provides standard programming capabilities as well as specialized capabilities for data ingestion, data handling, mathematical analysis and visualization. The core contains support for linear and generalized linear models, nonlinear regression, time series, clustering, smoothing and more. The language has been in development and use since 1997 with the 1.0 release coming in 2000. The core is now at release 3.0. New capabilities can be added by creating packages typically written in the R language itself. Over 5,000 packages have been added through the open source community.