Data Mining

Share your thoughts on advanced analytics

Hurwitz & Associates will update their Victory Index on Advanced Analytics in 2014. This report will summarize key trends in advanced analytics, provide information on vendor offerings, and report on vendor strengths and weaknesses from an end user perspective. In order to help guide end users who are making a decision on selecting an advanced analytics vendor, they would like to include feedback from current users. So, if you are a user of advanced analytics for use cases such as predicting consumer behavior, churn analysis, fraud analysis, predicting equipment failure, and reducing risk, they would really like to hear what you think. To help them understand how a broad group of users of advanced analytics solutions rate the capabilities and benefits of the products they use, go to and let them know.

And don’t forget you can see my revi

Standards in Predictive Analytics: A White Paper

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.

Thanks to our sponsors for this research, Revolution Analytics, Zementis and the Data Mining Group.

Another analytic practitioner speaks – an interview with Tracy Altman

Last year I interested Andrea Scarso, CEO of MoneyFarm, about analytics. This was a hugely popular post so I thought I would continue the series this year by interviewing some other analytic practitioners. The first in this continuing series is an interview with Tracy Allison Altman, co-founder of Ugly Research. Ugly Research are developing PepperSlice, […]

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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Oracle BIWA Summit January in Redwood Shores

I am giving the opening keynote for the BIWA Summit 2014 January 14-16, 2014 at the Oracle HQ Conference Center in Redwood Shores, CA. It’s an Oracle-focused event, obviously, with some novel and interesting use cases of Oracle Big Data, Exadata, Advanced Analytics/Data Mining, OBIEE, Spatial, Endeca and more. There are opportunities to get hands-on experience with products in the Hands-on Labs, great customer case studies and talks by Oracle Technical Professionals and Partners.  You can even schedule 1:1 time with me and other technical experts.   some interesting conference talks (besides mind) include:

  • Hands-on Lab:  Learn to use Oracle R Enterprise —

White Paper of the Week: Putting Predictive Analytics to Work 2014

This week’s white paper is Putting Predictive Analytics to Work

We have recently updated this white paper on using Decision Management to make your operational systems analytical and improve customer treatment.

Applying predictive analytics to operational decision making is the next wave of business innovation. Operational decisions drive interactions and determine the actions taken by an organization. Operational decisions are critical to how an organization treats, and is perceived by, its customers, its partners and its suppliers. Decision Management is the framework needed to fully exploit this opportunity.

You can get this white paper, and others, on our white paper download page. If you need some help getting started with Predictive Analytics or Decision Man

UCI Extension Defining Business Goals for Predictive Analytics – Winter 2014 enrollment open

I have been giving a class on Defining Business Goals for Predictive Analytics as part of the University of California Irvine Extension’s Predictive Analytics Certificate program. Predictive analytics can be used to seek out increasingly small margins and understand a company’s customers, products, channels, partners, and more. However, predictive analytics is only part of the […]

Clearly defined objectives for predictive analytic projects

There was a great article in Predictive Analytics Times recently by my friend Dean Abbott – A Good Business Objective Beats a Good Algorithm. Dean, like me, talks about the importance of the “three legged stool” of business, analytics and IT. But it was the title that particularly struck me. As Dean says, it’s easy to […]

Highlights of the Predictive Analytics in the Cloud Study

Check out James’ guest post over on KDNuggets for highlights of our recent Predictive Analytics in the Cloud Research Study. Here are a few of my favorites: Success breeds success: Early adopters with one or more predictive analytics solutions deployed in the cloud were significantly more likely to have plans to expand deployment and were […]

Build Your Big Data Analytics Capability with Decision Management

The old way: Big data is a hot topic. Big Data was coined as a term by Gartner to mean data that has Volume (more data), Variety (of many types) and Velocity (that arrives more rapidly). Value is extracted from big data using advanced analytics such as data mining and predictive analytics. This data can […]