My Digital FootPrint

Tony Fish @My Digital Footprint I have been working with several teams on data driven innovations and failing to create the value expected. Listed below are some mindsets which I have come across that appear to create hurdles to the data opportunity. The theme underlying these views, I believe, is the same as the old philosophy argument on behaviour and judgement which is “are you Fact or Opinion biased?”  Personally I am defiantly and firmly in the “tell me your opinion.” The Internet has allowed the world to find, no matter what the opinion, some data to support it.  As I can/will always find facts to back any story, I want to know what you’re thinking, why you hold that view and what drives you. Why do I think this; because strategy and innovation is a judgement on outcomes and not a science.  I find it interesting that many are trying to prove me right or wrong with their data, rather than accepting that my opinion is mine and it is neither right or wrong but based on

Source : Andrew McAfee’s Blog Source2 : Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach They analysed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males

  Pew Research Internet Project has released ( March 2014) a report on Digital Life in 2025 based on expert interviews.   The report marks the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee who released the code for his system, for free, to the world on Christmas Day in 1990.  The Web became a major layer of the Internet. Indeed, for many, it became synonymous with the Internet, even though that is not technically the case.   This report looks at the present and the past of the Internet, marking its strikingly fast adoption and assessing its impact on American users’ lives. This report is part of an effort by the Pew Research Center’s Internet Project in association with Elon University’s Imagining the Internet Center to look at the future of the Internet, the Web, and other digital activities. This is the first of eight reports based on a canvassing of hundreds of experts about the future of such things as privacy, cybersecurity, the “Internet of things

interrupting our free from communication is much more difficult than we think

Source : http://www.linkedin.com/today/post/article/20140122165800-37102839-lies-data-tell-us Great article from Steven Thomson He sets out questions to ask next time you're about to make a big decision based on a particular set of data: Are you measuring the right thing? In almost any data-gathering situation, there are far more types of information that could be gathered than you can possibly tackle. Compare the contradictory claims that U.S. wireless phone providers make for their network coverage. No one's lying--they're all just picking different aspects of coverage to measure. Are you measuring it accurately? There are far more ways to screw up a measurement than there are to get it right. Ever compare election results to what the polls had said right up to the end? And political pollsters are the rocket scientists of data gathering--it's downhill from there. Are you interpreting the data wisely? Unless someone is inside trading, all inve

Thinking about data and what it can tell us Is there an equation for intelligence? Yes. It's F = T ∇ Sτ. In a fascinating and informative talk, physicist and computer scientist Alex Wissner-Gross explains what in the world that means.

Source: http://blogs.hbr.org/2014/02/how-to-tell-if-someone-is-lying/ To accurately infer another’s intentions, you need to look for  a set  of cues — gestures that together can more accurately predict or reveal motivation. Here’s how my colleagues and I identified the four key ones (with the help of a robot, of course) and the outcome is that if you express these gestures, you are probably less trustworthy…. 1.        Hand touching 2.        Face touching 3.        Crossing arms 4.        Leaning away So What:   nothing that new, except that it was a robot which was the control, unlike previous studies. As Robots come into play more and more, the person who codes them can become the double bluff.  Trick you with emotions of love and steal your wallet in the process. Don’t believe me then watch – Guy Hoffman: Robots with "soul”

Source :  https://www.facebook.com/notes/facebook-data-science/the-formation-of-love/10152064609253859 This is the Facebook view of the world of relationships which start with a period of courtship on Facebook ( e.g messages are exchanged, profiles are visited, posts are shared on each other's timelines.) = snooping.  The graph shows the average number of timeline posts exchanged between two people who are about to become a couple. We studied the group of people who changed their status from "Single" to "In a relationship" and also stated an anniversary date as the start of their relationship. During the 100 days before the relationship starts, we observe a slow but steady increase in the number of timeline posts shared between the future couple. When the relationship starts ("day 0"), posts begin to decrease. We observe a peak of 1.67 posts per day 12 days before the relationship begins, and a lowest point of 1.53 posts per day 85 days int

By   NATASHA SINGER Source: http://www.nytimes.com/2013/04/28/technology/personal-data-takes-a-winding-path-into-marketers-hands.html The essence of this is article is that we don't know how or why something's happen in a digital world. Our data can go anywhere and is not in our control – nor is what others imply about us based on our data.  When it adds value we love it, when it does not we find it creepy

As machines take on more jobs, many find themselves out of work or with raises indefinitely postponed. Is this the end of growth? No, says Erik Brynjolfsson -- it’s simply the growing pains of a radically reorganized economy. A riveting case for why big innovations are ahead of us … if we think of computers as our teammates. Be sure to watch the opposing viewpoint from Robert Gordon.

Pew Internet  have released a new report  on Teens, Social Media and Privacy. Download Summary of Findings Teens share a wide range of information about themselves on social media sites; indeed the sites themselves are designed to encourage the sharing of information and the expansion of networks. However, few teens embrace a fully public approach to social media. Instead, they take an array of steps to restrict and prune their profiles, and their patterns of reputation management on social media vary greatly according to their gender and network size. These are among the key findings from a new report based on a survey of 802 teens that examines teens’ privacy management on social media sites: ·          Teens are sharing more information about themselves on social media sites than they did in the past. For the five different types of personal information that we measured in both 2006 and 2012, each is significantly more likely to be shared by teen social media use

Here’s a presentation  from James Morgan , Telefonica UK’s head of information strategy for business intelligence. Telefonica new ‘vision’: “To be The Most Trusted Provider of Brilliant Digital Experiences”   My problem is that this is all about control aka the Apple model and not an open model – implicit is that they know better than you about what you want!

KPCB Internet Trends 2013 from Kleiner Perkins Caufield & Byers

  Source http://www.fico.com/analytics   Predictive analytics is not a method by a diversity of methods brought together to enable businesses/ government/ individuals to make smarter, better informed, decisions.   “We know what you will do before you do” …. as long as we have the data!

Source for new thinking: http://blogs.hbr.org/cs/2013/06/a_new_framework_for_customer_s.html It started with… " You know, I don't think I believe in segmentation anymore." She said it fast and softly, almost in hope that the sounds around us would make it inaudible. But we did hear it, and responded, "Well, we don't either." From observing … " we were finding an increasing disconnect between telling people about segmentation, targeting and positioning on the one hand, and about the increasing shift of control from brands to consumers, on the other " HBR presents a new kind of segmentation based the combinations of jobs that customers need to get done.  Think Ted Levitt's famous comment about selling ¼ inch holes rather than ¼ inch electric drills. They provide this outline…. Step #1: Identify the contexts in which customers are using the company's products. Step #2: Combine information about transactions and cust

Main page is here http://www.kickstarter.com/projects/ajitjaokar/computer-science-for-your-child

I have written about the fact that Social Networks offer insights into how we humans interact with each other many times as they have unparalleled access to real time data. New  analysis from Wolfram Alpha , has examined usage habits and found that it as we expected, but have the data to prove it. Here’s a summary of some of the more notable findings, some of which are depressingly stereotypical, according to Wolfram Alpha designer Stephen Wolfram. The median number of Facebook friends is 342, a number that   varies based on how old you are : Teenagers tend to have more friends than adults do. When you’re younger, most of your friends are your own age , but the range of ages broadens as you get older. Teenage boys tend to have more friends than teenage girls, but that difference disappears as they get older. The older you get, the more likely you are to be married; women get married earlier than men; and, by 30, about 70 percent of people are married. (“It’s as if all those h

Image. It is an algorithm: globalimaging.com/images/modis-atmos.jpg Algorithms     are the foundation of your computing interactions. An algorithm is the means by which a computer program can make decisions about you, for you, or decisions that affect you.  Algorithms are the translation of what you do into rules and policies that a computer understands (i.e. 0s and 1s). Like it or not, you are influenced by them as much as you influence them. Algorithms need data, they use digital data that you give, leave or have tracked about you (willingly or not).  This input into an algorithm is your digital footprint, which comes from Facebook, Twitter, text messages, email, key stokes, swipes, gestures, play lists, payment records, your routes, navigation – indeed anything you do which is an interaction with an electronic device. This is the basis of what an algorithm knows about you. It is how an algorithm can model you, it takes input and predicates based on what you have done and

Researchers at Massachusetts Institute of Technology (MIT) and the Catholic University of Louvain studied 15 months' worth of anonymised mobile phone records for 1.5 million individuals. Here's  the full study. With no real surprise they found from the "mobility traces" - the evident paths of each mobile phone - that only four locations and times were enough to identify a particular user. We are predictable and so Dan Ariely Work comes true.  In their own words “ They studied fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resol