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  • futurist-foresight:

A look at data: Data is not always useful.
robbiethompson:

The Value of Big Data Isn’t the Data
“…I would argue that what you want and what you need is to turn that data into a story. A story explains the data rather than just exposing it or displaying it…” - Kristian J. Hammond

    futurist-foresight:

    A look at data: Data is not always useful.

    robbiethompson:

    The Value of Big Data Isn’t the Data

    “…I would argue that what you want and what you need is to turn that data into a story. A story explains the data rather than just exposing it or displaying it…” - Kristian J. Hammond

    Source: robbiethompson
    • 2 weeks ago
    • 24 notes
    • #bigdata
  • laughingsquid:

IT Crowd Creator Graham Linehan Bringing the Geeky British Sitcom Back For One Last Episode

    laughingsquid:

    IT Crowd Creator Graham Linehan Bringing the Geeky British Sitcom Back For One Last Episode

    Source: Laughing Squid
    • 2 weeks ago
    • 4239 notes
  • Who's Big in Big Data? (Infographic)

    #bigdata infographic

    • 1 month ago
  • ifthisthenth-ey:

Big data needs people, leaders and real-time analytics: A Structure:Data 2013 recap
In the afterglow of GigaOM’s Structure:Data conference this week, a few big-picture trends and surprising quotes stuck with us.
Data needs people, my friend
Despite the much-discussed power of data, there are roles for people to play in big data projects. Data increasingly influences companies’ decision making processes, but several speakers hit on the notion that people should be involved in big data storage and analysis.
It all starts with a human question. Before machines generate answers, employees from many departments should feel empowered to ask good questions of data, said John Sotham, vice president of finance at BuildDirect.
Beyond questions, humans need to decide which algorithms to employ and which data to use to answer questions, said Scott Brave, founder and chief technology officer of Baynote.

In data science, machine use algorithms to make decisions with clean data for the sake of prediction and optimization, said Sean Gurley, chief technology officer of Quid. But in “data intelligence,” humans “create, change and shape the world we’re in” using small sets of messy data, he explained.

Sometimes algorithms don’t bring the best results as well as people can. One website crowdsources identification of the top news to people, as my colleague Kevin Tofel wrote. And at times, it’s wise to throw lots of people at big data challenges. With TopCoder, there are competitions to discover the best software architecture, algorithms and analytics, said the company’s chief technology officer, Mike Lydon.
There was an exception to the man-and-machine rule. The software BeyondCore’s software makes machines crunch all available variables to isolate the biggest profit generators. It displays charts and audibly tells you its findings.
It takes leadership
Becoming a data-driven company requires a human push, said Paul Maritz, chief strategist at EMC. “Change requires leadership,” he said. “It requires people to understand what is happening and really get behind it and drive organizations to transform, because none of us really like to change,” he said. Only then can companies discover better ways to make money.
Meanwhile, Amaya Souarez, director of data center services at Microsoft, saidthat lots of internal data doesn’t automatically affect changes in strategy. “The data will help you in your discussions, but it’s not everything,” she said. “It really does take a lot of personal interaction and commitment to that relationship,” she said.
We want analytics and we want it now
Whether in Hadoop or in specialized databases, our speakers showed why they want to see big data analytics to happen in real time.
Muddu Sudhakar, vice president and general manager of the Pivotal Initiative’s Cetas cloud and big data analytics platform, called for “Hadoop high throughput, low latency.” And SQLstream CEO Damian Black said that 2013 “seems to be the year where it’s all happening now. All Hadoop distributions are talking about streaming technology.”
Ashok Srivastava, chief data scientist at Verizon, talked about what machines could do if they process data in real time: go through millions of new pictures users make on their cell phones and predict the health of a person or a machine based on changes over time. Similarly, Maritz identified an opportunity telecommunications companies have yet to take advantage of: texting customers to apologize for a dropped call. “They can’t even do that today, let alone do more ambitious things on top of that,” Maritz said.
Big data words to the wise
Executives, IT administrators and others will likely discuss these themes in the coming months. A few statements from speakers also stand out:
“…What’s really most intriguing is that you can be 100 percent guaranteed to be identified by simply your gait — how you walk.” — Ira “Gus” Hunt, chief technology officer of the CIA, in a statement on the capabilities of a three-axis accelerometer
“Hadoop is hard — let’s make no bones about it. It’s damn hard to use. It’s low-level infrastructure software, and most people out there are not used to using low-level infrastructure software.” — Todd Papaioannou, founder and CEO of Continuuity, in a statement on his lessons from Yahoo, where he was chief cloud architect
- “I get asked all the time to explain, How is Riak better than Hadoop?” — Justin Sheehy, chief technology officer of Basho Technologies, in a statement about how hype surrounding Hadoop and big data gets in the way of real discussion about solving data problems
- “What if you could send your sperm over email to somebody else and print the sperm on the other end?” — Naveen Jain, founder and CEO of Inome, in astatement about disruptions in big data from other industries
(via Big data needs people, leaders and real-time analytics: A Structure:Data 2013 recap — Tech News and Analysis)

    ifthisthenth-ey:

    Big data needs people, leaders and real-time analytics: A Structure:Data 2013 recap

    In the afterglow of GigaOM’s Structure:Data conference this week, a few big-picture trends and surprising quotes stuck with us.

    Data needs people, my friend

    Despite the much-discussed power of data, there are roles for people to play in big data projects. Data increasingly influences companies’ decision making processes, but several speakers hit on the notion that people should be involved in big data storage and analysis.

    It all starts with a human question. Before machines generate answers, employees from many departments should feel empowered to ask good questions of data, said John Sotham, vice president of finance at BuildDirect.

    Beyond questions, humans need to decide which algorithms to employ and which data to use to answer questions, said Scott Brave, founder and chief technology officer of Baynote.

    In data science, machine use algorithms to make decisions with clean data for the sake of prediction and optimization, said Sean Gurley, chief technology officer of Quid. But in “data intelligence,” humans “create, change and shape the world we’re in” using small sets of messy data, he explained.

    Sometimes algorithms don’t bring the best results as well as people can. One website crowdsources identification of the top news to people, as my colleague Kevin Tofel wrote. And at times, it’s wise to throw lots of people at big data challenges. With TopCoder, there are competitions to discover the best software architecture, algorithms and analytics, said the company’s chief technology officer, Mike Lydon.

    There was an exception to the man-and-machine rule. The software BeyondCore’s software makes machines crunch all available variables to isolate the biggest profit generators. It displays charts and audibly tells you its findings.

    It takes leadership

    Becoming a data-driven company requires a human push, said Paul Maritz, chief strategist at EMC. “Change requires leadership,” he said. “It requires people to understand what is happening and really get behind it and drive organizations to transform, because none of us really like to change,” he said. Only then can companies discover better ways to make money.

    Meanwhile, Amaya Souarez, director of data center services at Microsoft, saidthat lots of internal data doesn’t automatically affect changes in strategy. “The data will help you in your discussions, but it’s not everything,” she said. “It really does take a lot of personal interaction and commitment to that relationship,” she said.

    We want analytics and we want it now

    Whether in Hadoop or in specialized databases, our speakers showed why they want to see big data analytics to happen in real time.

    Muddu Sudhakar, vice president and general manager of the Pivotal Initiative’s Cetas cloud and big data analytics platform, called for “Hadoop high throughput, low latency.” And SQLstream CEO Damian Black said that 2013 “seems to be the year where it’s all happening now. All Hadoop distributions are talking about streaming technology.”

    Ashok Srivastava, chief data scientist at Verizon, talked about what machines could do if they process data in real time: go through millions of new pictures users make on their cell phones and predict the health of a person or a machine based on changes over time. Similarly, Maritz identified an opportunity telecommunications companies have yet to take advantage of: texting customers to apologize for a dropped call. “They can’t even do that today, let alone do more ambitious things on top of that,” Maritz said.

    Big data words to the wise

    Executives, IT administrators and others will likely discuss these themes in the coming months. A few statements from speakers also stand out:

    “…What’s really most intriguing is that you can be 100 percent guaranteed to be identified by simply your gait — how you walk.” — Ira “Gus” Hunt, chief technology officer of the CIA, in a statement on the capabilities of a three-axis accelerometer

    “Hadoop is hard — let’s make no bones about it. It’s damn hard to use. It’s low-level infrastructure software, and most people out there are not used to using low-level infrastructure software.” — Todd Papaioannou, founder and CEO of Continuuity, in a statement on his lessons from Yahoo, where he was chief cloud architect

    - “I get asked all the time to explain, How is Riak better than Hadoop?” — Justin Sheehy, chief technology officer of Basho Technologies, in a statement about how hype surrounding Hadoop and big data gets in the way of real discussion about solving data problems

    - “What if you could send your sperm over email to somebody else and print the sperm on the other end?” — Naveen Jain, founder and CEO of Inome, in astatement about disruptions in big data from other industries

    (via Big data needs people, leaders and real-time analytics: A Structure:Data 2013 recap — Tech News and Analysis)

    Source: gigaom.com
    • 1 month ago
    • 7 notes
  • “Less data science and more data art — which, in other words, means that data wranglers have to develop correlations between data much like the human brain finds context. It is actually not about building the fanciest machine, but instead about the ability to ask the human questions. It is not about just being data informed, but being data aware and data intelligent.”
    — Om Malik from Coffee & Empathy: Why data without a soul is meaningless (via yoonkeesull)
    Source: yoonkeesull
    • 1 month ago
    • 2 notes
  • Data Information Knowledge Wisdom

    We support the idea of taking data and converting it into information by organizing it, and then converting it into knowledge by adding context.  For businesses there is a huge effort to turn all their raw data into knowledge to provide better outcomes.  We think this is a good graphic that represents the idea from a visual standpoint. 

    • 2 months ago
    • 3 notes
    • #big data
    • #data visualization
    • #datascience
    • #datasimple
  • We have Fancy Math, now what?

    Our CEO, Timothy Mohn, will be speaking at The Strata conference in Santa Clara, CA this Tuesday FEB 26th, 2013.  Come by and say hello!

    • 3 months ago
    • 1 notes
    • #big data
    • #strata
    • #data science
    • #math
    • #datasimple
  • Building Data Scientist Capability

    Reposting an article

    • 3 months ago
    • #data science
    • #big data
  • “Are you going to be optimizing the solution to one problem, or solving a wide variety of problems? If it’s just one problem, is it something that you can imagine being happy about working on a year from now?”
    — Hilary Mason is on point, as usual. Read Finding a Great Data Science Job for more excellent considerations. (via adamlaiacano)

    (via adamlaiacano)

    Source: linkedin.com
    • 3 months ago
    • 5 notes
    • #data
    • #big data
    • #data science
    • #jobs
    • #advice
  • Data Stat:

    According to industry analysts, enterprise data growth over the next five years is estimated at 650 percent

    • 3 months ago
    • 1 notes
    • #data
    • #big data
    • #data science
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