image source : http://www.datagovernance.com/ - download the framework from http://www.datagovernance.com/dgi_framework.pdf or http://www.datagovernance.com/11x17_DGI_framework_poster_color.pdf

Data governance is not fully defined and should be seen as an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards and data custodians to handle data.

Data governance is at best a set of processes that ensures that important data assets are formally managed throughout the enterprise, ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. It is all about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient.

So I have read a lot about the shortage of data scientists…well on a scan of my LinkedIn 1st and 2nd connections I can see that outside of SFO there are not many ….. here is a selection worth watching.

Andreas Weigend Stanford and x Chief Scientist at Amazon.com

Dale Lord - Data Scientist at Moneysupermarket.com

Bob Rapp - Center of Excellence Data Center Transformation and Private Cloud at Microsoft

Christian Posse - Principal Data Scientist at LinkedIn

Hugo Gävert - Senior Data Scientist

Jim Porzak - Senior Data Scientist at Viadeo

Petri Kärkäs -  Data Scientist at Nokia

Bryan Gumm  -Senior Data Scientist @ Netflix

Jeff Hammerbacher - Chief Scientist, Cloudera

Pat Hanrahan - chief scientist and co-founder, Tableau Software

Monica Rogati - Senior Data Scientist, LinkedIn

Peter Skomoroch - Principal Data Scientist at LinkedIn

DJ Patil, Data Scientist in Residence Greaylock Ventures

Sebastian Thrun - Professor, Stanford University

Peter Norvig - Data Scientist, Google

Elizabeth Warren - Candidate, U.S. Senate (Massachusetts)

Todd Park - CTO, Department of Health and Human Services

Alex "Sandy" Pentland, Professor, MIT

Michael Schmidt - Computer Scientists, Cornell University

Chris Diehl  - Senior Data Scientist, Jive Software

Daniel Tunkelang - Principal Data Scientist at LinkedIn

Steven Hillion - Vice President of Analytics at EMC Greenplum

John Rauser - principal engineer Amazon's

Michael Driscoll Co-founder,  Metamarkets

Amy Heineike - Director of Mathematics, Quid

And Larry Page

http://www.chetansharma.com/2012_Mobile_Industry_Predictions_Survey.pdf

2011 was a terrific year for the mobile industry. With all its ups and down, consumers embraced devices, applications, services, and technology with more gusto than ever before. In the waning hours of 2011, we crossed the 6 billion subscriptions milestone. While the first billion took 19 years, this last billion only took 15 months.

Smartphones are selling like hot cakes. We estimate that by the end of Q4 2011, over 60% of the devices sold in the US were smartphones and over 30% of the global sales were for the evolved brethren of the primordial featurephones. Sparked by insatiable consumer demand for mobile data, LTE and HSPA+ networks are sprouting all over the planet with US leading the charge for broadband deployment.

Original Post = http://chucksblog.emc.com/chucks_blog/2011/12/understanding: the: new: rock: star: the: emc: data: science: survey.html

If Big Data is Big then the race is now on to acquire and maximize the productivity of the key talent behind this wave: data scientists and their supporting data science teams.

"We live in a data-driven world. Increasingly, the efficient operation of organizations across sectors relies on the effective use of vast amounts of data. Making sense of big data is a combination of organizations having the tools, skills and more importantly, the mindset to see data as the new "oil" fuelling a company. Unfortunately, the technology has evolved faster than the workforce skills to make sense of it and organizations across sectors must adapt to this new reality or perish." Andreas Weigend, Ph.D Stanford, Head of the Social Data Lab at Stanford, former Chief Scientist Amazon.com

Key Findings from the report

• Informed Decision: making: Only 1/3 of respondents are very confident in their company's ability to make business decisions based on new data.
• Looming Talent Shortage: 65% of data science professionals believe demand for data science talent will outpace the supply over the next 5 years – with most feeling that this supply will be most effectively sourced from new college graduates.
• Barriers to Data Science Adoption: Most commonly cited barriers to data science adoption include: Lack of skills or training (32%) budget/resources (32%), the wrong organizational structure (14%) and lack of tools/technology (10%).
• Customer Insights: Only 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers.
• New Technology Fueling Growth: 83% of respondents believe that new tools and emerging technology will increase the need for data scientists.
• Lack of Data Accessibility: Only 12% of business intelligence professionals and 22% of data scientists strongly believe employees have the access to run experiments on data – undermining a company's ability to rapidly test and validate ideas and thus its approach to innovation.
• Advanced Degrees: Data scientists are 3 times as likely as business intelligence professionals to have a Master's or Doctoral degree.
• Augmenting Business Intelligence: Although respondents found an increasing need for data scientists in their firm, only 12% saw today's business intelligence professionals as the most likely source to meet that demand.
• Higher: Level Skills: Data scientists require significantly greater business and technical skills than today's business intelligence professional. According to the Data Science Study, they are twice as likely to apply advanced algorithms to data, but also 37% more likely to make business decisions based on that data.
• Love the Work: The study discovered highly favorable attitudes toward the companies where they work. In fact, data scientists believe their IT functions are better aligned and better able to attract talent, are ahead in key technology areas like cloud computing and not surprisingly rate their company's data analysis and visualization abilities very favorably compared to the views of business intelligence professionals.
• Involved Across the Data Lifecycle: Data scientists are more likely than business intelligence professionals to be involved across the data lifecycle:  from acquiring new data sets to making business decisions based on the data. This includes filtering and organizing data as well as representing data visually and telling a story with data.
• Tools of the Trade: Data scientists are more likely than business intelligence professionals to use scripting languages, including Python, Perl, BASH and AWK. Yet, Excel remains the tool of choice for both data scientists and business intelligence executives, followed closely by SQL.

The full study is here : http://www.emc.com/collateral/about/news/emc-data-science-study-wp.pdf

Google has unique insight into the spirit of the times from a good % of the world. Trending Google searches of the year are a glimpse into what's we have been draw towards by “traditional media” and amplified by digital.  For the past 10 years, Google has published a year-end Zeitgeist report on the major search trends around the world.

Whilst the number one trending search was Rebecca Black (fully digital not traditional push) this years site is dynamic, detailed and easy to explore. Drilling down by region reveals some timely insights into what interested the wired world in 2011.

Your digital footprint is part of this data……

Introducing the idea of CaaS “Crime as a Service”

Why rob a bank “that is where the money is”

Why worry about Big Data “that is where the value is”

Marc Goodman is a global thinker, writer and consultant focused on the profound change technology is having on crime security, business and international affairs. Over the past 20 years, he has built his expertise in cyber crime, cyber terrorism and critical infrastructure protection working with organizations such as INTERPOL, the United Nations and NATO. Marc frequently consults with global policy makers, security executives and industry leaders on technology-related security threats and has operated in nearly seventy countries around the world.

From Strata Summit http://strataconf.com/summit2011/public/schedule/detail/20975

Image source www.stickycomics.com

Right now I have way too many applications that interact with each other from Twitter and Facebook for login credentials to many sites, deep integration with PayPal, Google and Posterous, and how my credibility, reputation or influence as measured by Klout or PeerIndex will change depending on what I connect.  As my social media tools has swopped from the destination (connections and updates) to authentication, so in my laziness I have become stuck (in a rut). I want a new key stoke to the above; “Laziness”

But I find division and confusion reign in all directions. As I post from my Blog here at My Digital Footprint, as this gets carried across G+, Linkedin, Twitter and Facebook – comments come back from every angle which means spreading the message is easy (distribution and broadcast), collation of comments input and feedback is hard and difficult.  Everything is linked but just enough to make it sticky, if Sticky is too difficult to move (e.g Bank Account) - then I am stuck.

I am not worried about getting my data back (download/ backup) or deleting it.  I am concerned that I no longer use some sites, but as they are linked together I still need them, but have no idea about what connections are where.

Here is a Christmas project for someone.  Can you write a visualisation that runs in Chrome that shows all my social media connections and linkages (application level), data passed etc and then what I can remove or what I could change?

What I am thinking is that I would like to be able to use two of three login credentials and that I can choose which ones to use at any time. 

Mobile usage data capture service Carrier IQ is installed on phones, collecting your data and continues it passage of discovery as we ourselves discover what is and what is not acceptable (with our data and with/ without our permission) Think Phorm for an earlier case study.

Evidence from my research that currently running that 90% think there is no difference between collected (harvested) and shared (given) data.

Carrier IQ is software that delivers data about peoples' cell phone use to the cellular network carriers. Dropped calls, call quality and app usage patterns and individual keystrokes.  This is the same data that drives data mining tools and analysis for your apps to be FREE as it is sold to marketing companies.  Think Flurry, comscore etc.  The value of the mobile is not the consumption of service on our phone but the volume of data our phone produces about us.  Think geo-tagged transaction data. 

Data is being understood, according to some leading analysts, as an economic input of equivalent importance to capital and labour. 

An important question is not whether or not this data will be collected and used - the question is who will HAVE RIGHTS TO USE DATA? Assume (safe) that no-one will have control! 

The full words are also here : http://blog.infochimps.com/2011/11/04/the-past-present-and-future-of-data/