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.

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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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What the data world can learn from the fashion industry

How generating conversations can become one of the most important data assets for any organization.

FD_coverAt O’Reilly Research, we focus our attention on trends in technology adoption — which tools are adopted and in which industries. In doing so, we uncover interesting cross-disciplinary opportunities and discover what we can learn from innovations in other fields.

We’ve recently learned about the increasing role of data in the fashion industry, so we set out to uncover some of the players who are making disruptive changes using technology and analytics.

Our team asked Liza Kindred, founder of Third Wave Fashion, and Julie Steele, coauthor of Beautiful Visualization and Designing Data Visualizations, to take a closer look at these developments in their new report, “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” We think you’ll find some surprising applications of data and analytics in the fashion industry — applications that are useful regardless of the industry or organization you work within. And, we know we’re just at the beginning of what is likely a growing trend. Read more…

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Big data’s move to the cloud

A new survey shows the market is ready for cloud-based big data services.

Migrating_big_data_analytics_cover_lg_2One night when our son was two years old, he abruptly decided that he didn’t like taking baths. As my wife recalls, he struggled mightily against the ritual of bathing for several months until, suddenly and mysteriously, he decided that he liked bathing again. We’re happy to report that he has managed to stay relatively clean ever since.

When I speak with CIOs and other IT leaders about moving big data operations into the cloud, I am reminded of our son’s unexplained loathing of the bathtub.

Nearly everyone associated with IT understands that most IT operations — including big data analytics — must eventually move into the cloud. The traditional on-premises approaches are simply too costly, and CIOs are under crushing pressure to shift budgetary resources to value-added, customer-facing activities.

For most companies, the writing is already on the wall. The cloud offers greater agility and elasticity, and quicker product development cycles — and can reduce costs. When you add up the benefits, it seems inevitable that the bulk of IT operations will move into the cloud. Nevertheless, the foot-dragging and excuse-making continues. Read more…

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Challenges facing predictive APIs

Solutions to a number of problems must be found to unlock PAPI value.

Key_in_Lock_nikolajnewyork_FlickrIn November, the first International Conference on Predictive APIs and Apps will take place in Barcelona, just ahead of Strata Barcelona. This event will bring together those who are building intelligent web services (sometimes called Machine Learning as a Service) with those who would like to use these services to build predictive apps, which, as defined by Forrester, deliver “the right functionality and content at the right time, for the right person, by continuously learning about them and predicting what they’ll need.”

This is a very exciting area. Machine learning of various sorts is revolutionizing many areas of business, and predictive services like the ones at the center of predictive APIs (PAPIs) have the potential to bring these capabilities to an even wider range of applications. I co-founded one of the first companies in this space (acquired by Salesforce in 2012), and I remain optimistic about the future of these efforts. But the field as a whole faces a number of challenges, for which the answers are neither easy nor obvious, that must be addressed before this value can be unlocked.

In the remainder of this post, I’ll enumerate what I see as the most pressing issues. I hope that the speakers and attendees at PAPIs will keep these in mind as they map out the road ahead. Read more…

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