analytics

IBM #WatsonAnalytics Live Blogging Series

James live blogged from this week’s IBM Watson Analytics event in New York earlier this week. Don’t miss the series posted to www.jtonedm.com. Table of contents for IBM Watson Analytics Cloud IBM Watson Analytics Cloud Announcement KickOff IBM Watson Analytics Cloud Customer Panel IBM Watson Analytics Cloud Business Partner Panel IBM Watson Analytics Cloud – […]

New Decision Management MarketScape from IDC

Brian McDonough has been writing (with Dan Vesset, Steve Hendrick, Henry Morris and others) on Decision Management at IDC for many years and he has recently published an IDC MarketScape: Worldwide Decision Management Software Platform 2014 Vendor Assessment. The report has an IDC MarketScape figure, IDC’s opinion on the Decision Management market, process and software as well as some […]

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 […]

Requirements for Enterprise Scale Analytics with R – Part 1

I recently did some research on the requirements for enterprise-scale analytics and the challenges of using open source R in this context. In this first post I wanted to outline some of the requirements I see for enterprise scale analytics and in a second post I will discuss the challenges of R in that context. As advanced […]

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 […]

Decision Management at the Building Business Capability Conference 2014

Join us at the Building Business Capability Conference (@BBCapability) in Fort Lauderdale in November for a pre-conference tutorial on Decision Modeling with DMN and a case study presentation on Decision-Centric Dashboards with DMN with Assure Corporation. Decision Modeling with DMN: Extending BPMN to Decisions, Business Rules and Analytics This workshop introduces the powerful technique of […]

Some thoughts on KDNuggets’ top trends ahead of Strata Hadoop NYC

Gregory over at KDNuggets had an interesting post with some Top Analytics and Big Data trends ahead of Strata Hadoop NYC Conference based on input from their readers. Three trends struck me: The challenge of communicating complex analyses to non-technical clients/partners We are having increasing success using logical decision models to show how data and analytics drive better […]

Decision Management and a business perspective on Big Data

Check out this article – Business leaders need Rs not Vs: The 5 Rs of Big Data. I met Merritt,the author, at the TDWI Solution Summit on Advanced Analytics this spring. In the post, and in his subsequent work, he makes some great points. I really like his 5Rs and think they illustrate the power of […]

Live Event: Insurance Analytics Symposium 2014: Industrializing Analytics

I am speaking on Industrializing Analytics for Better Decisions Everywhere at the Insurance Analytics Symposium in New Orleans, October 17.

Insurers of all sizes know they must adopt analytics, especially predictive analytics, to maximize the value of their customers, manage risk and compete effectively. One-off, ad-hoc approaches to analytics can demonstrate the value of analytics but are no basis for ongoing success. To succeed, Insurers need to industrialize their approach to analytics, making it integral to day-to-day operations.

In this session leading expert James Taylor will share the three keys to success based on his experience of helping insurers industrialize their analytic efforts and so u

The Economist: Big Data, Little Decisions

There was a fascinating piece in the Economist last week – Little things that mean a lot. This piece really resonated with me – even the title sounds a lot like my mantra of “Big Data, Little Decisions” (you can see a selection of the articles and webinars I have given on this topic here). So what were the critical points Schumpeter made?

First the point that constant experimentation and rapid iteration is critical when trying to get value from all this data. Experimentation is often the skill we tell clients they most need to develop and we regularly stress the importance of putting in place the infrastructure and processes for ongoing decision results analysis. To maximize the value of big data and beco