The Journey To Big Data Analytics - so here http://chucksblog.emc.com/chucks_blog/2012/01/the-journey-to-big-data-analytics.html is a blog from Chuck Hollis

VP, Global Marketing CTO EMC Corporation.  It is kind of interesting that they look at gathering data, storing data, analysing data and creating value aka the Digital Footprint business model - however they don't close the loop and complete the story.  They don't look into how to build it as a business for yourself and not them.  Great at selling a solution to the point where is has value for someone else but no growth for you.....  However it does put together the story, data scientist and what data if you need some back group.

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.

Irrespective of the wrong motivations, we don’t need more security. We do need to talk to our kids more and to be more open. Kids like to be in a place where adults don’t have control so they can explore – we have all done it. We also need to wake up and understand that we cannot control the future or bring our fears to stop the next gernation taking over from us.  But we love to think we are in control!

This is a picture of the world, as connected by Twitter created by Eric Fischer  @enf and shows people's virtual and physical networks It shows where people travel--and, what's more, who they communicate with all around the world. Thus, in one map, you can see where people's physical communities are, and their virtual ones as well.

There are a load more of this here with some explanation http://www.fastcodesign.com/1665780/infographic-of-the-day-mapping-the-worlds-tweet-networks

So data can tell us what we know

<p></p><p>Help for Depression</p>

The full post of Tushar Nawale original article on SmartData  and it is worth reading. I liked this chart showing some of the enterprise opportunities and the full article that brings out many of the other models and uses of data.  It takes the stance that you are in a corporate (demanding a use case) and need to justify exploring the value of big data and how to go about it. Whilst it therefore misses out on start-up land; it provides a useful framework.

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

Image source : http://memeburn.com/2011/03/why-data-is-the-new-oil/

There are a lot of trends for 2012 about data being the new gold, oil currency or something other commodity. Even if we ignore the basic economics about physical commodities (they are limited) raw data has some value, moving along the refinement process we know that filtered data has more value, presented data has even more value but data analysis that tells the story and the future has more, but the highest value is that which changes a behaviour.  Assuming that everyone now has some digital influence that can create behaviour or attitude change (not just celebrities, academics, policy makers or those who sway public opinion) - will we see in 2012 an influence reward system that will yield wealth and value on those with the most digital social influence or just more about how to score it?