data

Building the Modern Business Architecture, Decision by Decision

How your organization makes decisions drives the rest of the business environment – processes, events, data and the org-chart. A decision-centric view of a Modern Business Architecture is an essential organizing principle to deal with the data-driven, today’s knowledge-based economy. First, clarify the “modern” view on Business Architecture There is no generally accepted definition or […]

How to frame requirements for predictive analytic projects with decision modeing

One of my big focus areas right now is using decision requirements models to frame predictive analytic projects. We are working with a few insurance companies as well as a couple of manufacturing companies to apply decision requirements modeling in their analytic requirements process and I am excited by the potential. I recently wrote an […]

Framing Requirements for Predictive Analytics Projects with Decision Modeling

A big focus area right now for organizations looking to adopt or expand operational predictive analytics is using decision requirements models to frame requirements for their predictive analytic projects. We are working with a few insurance companies as well as a couple of manufacturing companies to apply decision requirements modeling in their analytic requirements process and I […]

Analytics – An Overwhelming Shift to Decision Making

It’s clear when you analyze analytic capabilities that there are three main reasons people use analytics: A need to report on some aspect of the organization. A need to monitor the organization’s behavior or performance. A need for the organization to make data-driven decisions. As part of our recently completed research on the analytic capability landscape, we […]

Challenges Scaling Open Source R – Part 2

I recently did some research on the requirements for enterprise-scale analytics and the challenges of using open source R in this context. In my first post (Requirements for Enterprise Scale Analytics with R – Part 1) I outline some of the requirements I see for enterprise scale analytics. In this second post I will discuss the challenges of R […]

MITSloan Article: Four Traps of Predictive Analytics

Michael Fitzgerald wrote a great piece of the MIT Sloan Management Review this week – The Four Traps of Predictive Analytics. Michael saw me present at an event for Predixion in Boston and then went on to chat with me a couple of times. It’s a great piece, highlighting four critical issues around predictive analytics: The […]

Ugly Research: Data is easy, Deciding is hard

Tracy Allison Altman over at Ugly Research has a great new  white paper – Data is easy: Deciding is hard – in which she quotes me (thanks Tracy). It’s a great paper and makes what I think is the critical point – that you don’t need a data culture but a decision culture. And I would add that you need this at every level – strategic, tactical and operational. The paper has some great advice and I would add a couple of additional thoughts:

For decisions you make often – some tactical and all operational decisions for instance – build a decision model so you know how you think you are going to/should make the decision moving forward. Here at Decision Management Solutions we use the new Decision Model and Notation (DMN) Standard and our modeling

TDWI Executive Summit: Moving to Real-Time Analytic Decision Making with Decision Management

I am speaking on Moving to Real-Time Analytic Decision Making with Decision Management at the TDWI Executive Summit in Boston, July 21-23

The right time to make decisions for organizations is increasingly real time. Customers want responses in real time; supply chains must adapt to disruption in real time; fraud must be caught before it gets into the system while self-service and Web applications can’t wait for human intervention. At the same time, organizations have discovered the value of analytic, data-driven decisions. The challenge is to reconcile these demands—to provide real-time, analytic decision making.

In this sess

Analytic practitioners speak – an interview with Lee Feinberg of DecisionViz

Please describe your current role and title

DecisionViz is a management consultant that helps companies build leadership in the processes, people, and culture around data visualization. We work with senior management to elevate visualization from the activity of reporting and making charts to being integrated with their daily decision-making. I founded the company in 2012.

What’s your background, how did you come to be working in analytics

Our story I think is like many in the analytics field. It’s been more of an evolution and the company has been almost 20 years in the making.
I was very involved with the Internet before most people heard about the Internet. So we were dealing with how to capture and measure all this new data. I’ve always been on the lookout for how to make that a lot less painful. Most of the work was in financi

Standards in Predictive Analytics: R

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.

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