Here is this week's roundup of data coverage, events, papers, and more!
In Better medicine, brought to you by big data , Derrick Harris at GigaOM references an August 2011 GigaOM post about a company called Tableau that is helping staff at the Seattle Children's Hospital "draw inferences from the 10 terabytes of data locked up in his servers and warehousing appliances." The goal is to streamline processes, plan trials, and increase hospital efficiency.
Pew Internet & American Life Project released a report on big data this week: The Future of Big Data . The report is based on findings from a survey conducted with Elon University. Elon University has made both the credited and anonymous survey responses available on their website, which provides a glimpse into how people are thinking about the potential impact of data-driven everything.
Survey participants were asked, “What impact will Big Data have in 2020? What are the positives, negatives, and shades of grey in the likely future you anticipate? How will use of Big Data change analysis of the world, change the way business decisions are made, change the way that people are understood?”
A couple interesting answers:
“The more that data sets are open and accessible, entrepreneurial Web-savvy types will harness that raw material for different ends, and many times these may be philanthropic. We will see more visual representations of large data sets that will enable people to see the impacts of their activities as they play out in other parts of the world. Big Data will be used to forecast and predict, more simulations will be played out, and these simulations will help people to understand the complexity of our correlation to each other, as beings on this planet and beyond. People will try to ‘fix’ or ‘game’ scenarios based on simulations. We’ve already seen this in the past decade with the Wall Street crisis, but systems of this size and complexity are dynamic and self-regenerative. The realization of dynamic and emergent systems as a natural order will cause people to realize the foolishness of trying to game systems to the Nth degree. We will see the rise of more algorithmic thinking among average people, and the application of increasingly sophisticated algorithms to make sense of large-scale financial, environmental, epidemiological, and other forms of data. Innovations will be lauded as long as they register a blip in the range of large-scale emergent phenomena.”
“The data deluge is a good thing. Large datasets can be mined and visualized to elucidate patterns that have not been ever possible. The availability of large datasets is not the issue, but how people choose to use this information for policy decisions that benefit humanity (or not.) As an example of large datasets, scientists continue to collect voluminous data from imaging techniques from earthquake and ocean sensors, satellites, and space probes. The 2D and 3D visualizations are able to help scientists explain changes to our planet and predict future changes. Inferential software can be wrong, but I believe the developers of those tools will be making tools for the greater good, so better software will be developed when bugs are found.” In "Why Big Is Bad When It Comes To Data ," InformationWeek's Patrick Houston takes a look at the shortcomings of "big data" as a buzzphrase. He argues that "big" merely describes the amount and makes it sound "too benign" given the complicated landscape of policy issues relating in a data-driven world. He writes:
Data isn't static, like standing waters of a reservoir. It's increasingly dynamic, generated and collected in real time. Even transactional data is being captured at both ends--and at every point in between. Ergo, data gushes.
In "Time To Build Your Big-Data Muscles ," Marcia Conner, a member of Fast Company's "expert contributor community," discusses the increasing demand for professionals with data analytics skills. Marcia argues that this isn't just for students getting ready to find jobs, either. Longtime professionals should start thinking about ways to brush up on their data analytics skills in order to remain competitive and relevant in the job market in any discipline, but especially in fields where business and IT come together. She writes, "Data will not answer questions by itself. People need to be able to communicate effectively about the findings, linking analytics to key decisions and the bottom line."
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