Parse.ly Blog

  • Apache Storm: The Big Reference

    Apache Storm is a free and open source project that is heavily used here at Parse.ly, as well as at other major real-time data processing projects such as Twitter, Pinterest, Spotify, and Wikipedia. In the last year, a flurry of digital documentation has been released about Storm, as the project gained traction in the commercial…

    Andrew Montalenti

  • Newsrooms Explain How to Make Data Talk

    As analytics become more and more common in digital newsrooms, the conversation has shifted from “Should we use analytics,” to “What are analytics actually telling us about our audience?” It’s a  difficult question to answer, with the only consensus appearing so far is that there is no consensus. Digital publishers have different audiences;  their business…

    clareondrey

  • Hacking The News

    A Sunday morning in the MIT Media Lab atrium, all the clues were there. Few words spoken between huddled teams. Quiet determination encoded in methodical keystrokes. Yes, a hackathon was entering its final hours. Hacking Journalism, a hackathon hosted by Embed.ly & MIT’s Media Lab brought together journalists, students and technologists to hunt for solutions…

    parsely

  • How to tell if explanatory journalism is explaining anything to anyone

    The new cohort of explanatory journalism sites have been up and running long enough to take a first look at if they’re meeting expectations or not. But with digital publishers of all stripes struggling to understand what success means online, the added complexity of these new models only makes evaluation more difficult. Getting clear audience…

    parsely

  • Alex Leo from Newsweek on the art and science of measurement in the digital newsroom

    There’s a frustrating,  basic truth about data and analytics: the information provided is at best noise, and at worst, harmful if not correctly analyzed and interpreted. Often, data scientists discuss how they must both be able to apply academic rigor while being creative enough to tell a narrative with the data. With the influx of…

    parsely

  • What the New York Times found out by mapping audiences’ reading paths

    Most media industry professionals have, hopefully, spent some time pouring over the digital innovation report from the New York Times. Throughout it, technology being at odds with “the way things have always been done” at traditional publishers becomes a recurring theme. A summary of the Package Mapper story One example: Director of News Analytics, James…

    clareondrey

  • Why loyalty is the great equalizer of content metrics

    It always comes back to metrics. We’ve written on which metrics matter, which ones might not, and there’s been extensive debate from others on the same topic. Digital content teams are investing to understand their metrics — the Association of Online Publishers found that 89% of their survey respondents planned to grow their investment in…

    clareondrey

  • Musings on Product Management

    A little over two  years ago I decided my current career in digital / business analytics just wasn’t cutting it. I enjoyed solving problems through data and even exploring the overhyped big data space, but I was also frustrated being pigeonholed as just an “analytics” guy when I felt I could be doing so much…

    mikesukmanowsky

  • streamparse: Python and Apache Storm made easy with a new open source project

    Our engineering team released  streamparse today at PyData Silicon Valley 2014. It was demonstrated at our talk,    “Real-time streams & logs with Apache Kafka and Storm”. We discussed Apache Storm, Apache Kafka, and the Python libraries we use to interact with those tools, such as the samsa Kafka driver, which we maintain. These tools…

    Andrew Montalenti

  • Q&A with Dan Lyons, on which metrics actually matter to writers

    The upheaval of the  media industry has been accompanied by well-publicized worries that measuring writers’ performance using data will lead to creative decline.  Some publications  make a point of shielding their writers from this data, while others compensate on various metric benchmarks. This debate has been largely among editor ranks, so we wanted to hear…

    parsely