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Commodity data analytics for health care

Predixion service could signal a trend for smaller health facilities.

Analytics are expensive and labor intensive; we need them to be routine and ubiquitous. I complained earlier this year that analytics are hard for health care providers to muster because there’s a shortage of analysts and because every data-driven decision takes huge expertise.

Currently, only major health care institutions such as Geisinger, the Mayo Clinic, and Kaiser Permanente incorporate analytics into day-to-day decisions. Research facilities employ analytics teams for clinical research, but perhaps not so much for day-to-day operations. Large health care providers can afford departments of analysts, but most facilities — including those forming accountable care organizations — cannot.

Imagine that you are running a large hospital and are awake nights worrying about the Medicare penalty for readmitting patients within 30 days of their discharge. Now imagine you have access to analytics that can identify about 40 measures that combine to predict a readmission, and a convenient interface is available to tell clinicians in a simple way which patients are most at risk of readmission. Better still, the interface suggests specific interventions to reduce readmissions risk: giving the patient a 30-day supply of medication, arranging transportation to rehab appointments, etc. Read more…

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Use data or be data

Trina Chiasson argues that data has arrived at the same threshold as coding: code or be coded; learn to use data or be data.

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Trina Chiasson

Arguments from all sides have surrounded the question of whether or not everyone should learn to code. Trina Chiasson, co-founder and CEO of Infoactive, says learning to code changed her life for the better. “These days I don’t spend a lot of time writing code,” she says, “but it’s incredibly helpful for me to be able to communicate with our engineers and communicate with other people in the industry.”

Though helpful for her personally, she admits that it takes quite a lot of time and commitment to learn to code to any level of proficiency, and that it might not be the best use of time for everyone. What should people commit time to learn? How to use data. Read more…

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Signals from Strata + Hadoop World New York 2014

From unique data applications to factories of the future, here are key insights from Strata + Hadoop World New York 2014.

Experts from across the data world came together in New York City for Strata + Hadoop World New York 2014. Below we’ve assembled notable keynotes, interviews, and insights from the event.

Unusual data applications and the correct way to say “Hadoop”

Hadoop creator and Cloudera chief architect Doug Cutting discusses surprising data applications — from dating sites to premature babies — and he reveals the proper (but in no way required) pronunciation of “Hadoop.”

Read more…

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What happens when fashion meets data: The O’Reilly Radar Podcast

Liza Kindred on the evolving role of data in fashion and the growing relationship between tech and fashion companies.

Editor’s note: you can subscribe to the O’Reilly Radar Podcast through iTunes, SoundCloud, or directly through our podcast’s RSS feed.

In this podcast episode, I talk with Liza Kindred, founder of Third Wave Fashion and author of the new free report “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” Kindred addresses the evolving role data and analytics are playing in the fashion industry, and the emerging connections between technology and fashion companies. “One of the things that fashion is doing better than maybe any other industry,” Kindred says, “is facilitating conversations with users.”

Gathering and analyzing user data creates opportunities for the fashion and tech industries alike. One example of this is the trend toward customization. Read more…

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Fast data fuels real-time streaming applications

A new report describes an imminent shift in real-time applications and the data architecture they require.

Fast_data_coverThe era is here: we’re starting to see computers making decisions that people used to make, through a combination of historical and real-time data. These streams of data come together in applications that answer questions like:

  • What news items or ads is this website visitor likely to be interested in?
  • Is current network traffic part of a Distributed Denial of Service attack?
  • Should our banking site offer a visitor a special deal on a mortgage, based on her credit history?
  • What promotion will entice this gamer to stay on our site longer?
  • Is a particular part of the assembly line overheating and need to be shut down?

Such decisions require the real-time collection of data from the particular user or device, along with others in the environment, and often need to be done on a per-person or per-event basis. For instance, leaderboarding (determining who is top candidate among a group of users, based on some criteria) requires a database that tracks all the relevant users. Such a database nowadays often resides in memory. Read more…

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The human side of Hadoop

Doug Cutting on applications of Hadoop, where "Hadoop" comes from, and the new partnership between Cloudera and O'Reilly.

Roger Magoulas, director of market research at O’Reilly and Strata co-chair, recently sat down with Doug Cutting, chief architect at Cloudera, to talk about the new partnership between Cloudera and O’Reilly, and the state of the Hadoop landscape.

Cutting shares interesting applications of Hadoop, several of which had touching human elements. For instance, he tells a story about visiting Children’s Healthcare of Atlanta and discovering the staff using Hadoop to reduce stress in babies. Read more…

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