Social media was a bigger part of the election season of 2012 than ever before, from the enormous volume of Facebook updates and tweets to memes during the Presidential debates to public awareness of what the campaigns were doing there in popular culture. Facebook may even have booted President Obama’s vote tally.
While campaigns have a public presence that is mostly recorded and observed, the stuff that goes on behind the scenes is so much more sophisticated than it has been. In 2008 we were fascinated by the Obama campaign’s use of iPhones for data collection; now we’re entering an age where campaigns don’t just collect information by hand, but harvest it and learn from it. An “information arms race,” as GOP consultant Alex Gage puts it.
For most news organizations, the standard approach to campaign coverage is tantamount to bringing a knife to a gun fight. How many data scientists work for news organizations? We are falling behind, and we risk not being able to explain to our readers and users how their representatives get elected or defeated.
Writing for the New York Times today, Slate columnist Sasha Issenberg revisited that theme, arguing that campaign reporters are behind the curve in understanding, analyzing or being able to capably replicate what political campaigns are now doing with data. Whether you’re new to the reality of the role of big data in this campaign or fascinated by it, a recent online conference on the data-driven politics of 2012 will be of interest. I’ve embedded it below:
Issenberg’s post has stirred online debate amongst journalists, academics and at least one open government technologist. I’ve embedded a storify of them below.
Yesterday, I published an interview with Michael Flowers, New York City’s director of analytics for the Office of Policy and Strategic Planning in Mayor Bloomberg’s office. In the interview, “Predictive data analytics is saving lives and taxpayer dollars in New York City,” Flowers talks about how his team of 5 is applying data analysis on the behalf of citizens to improve the efficiency of processes and more effectively detection of crimes, from financial fraud to cigarette bootlegging.
After our interview, Flowers followed up over email to tell me about a new working group on data analytics between New York City, Boston, Chicago and Philadelphia. The working group, which recently launched a website at www.g-analytics.org, is sharing methodologies, ideas and strategies,
“Ultimately we want the group to grow and support as many cities interested in pursuing this approach as possible,” wrote Flowers. “It can get pretty lonely when you pursue something asymmetrical or untraditional in the government space, so we felt it was important to make it as simple as possible for like-minded cities to get started. There’s a great guy I work closely with out in Chicago on this effort – [Chicago chief data officer] Brett Goldstein; we talk at least twice a week.”
In 2012, making sense of big data through narrative and context, particularly unstructured data, is now a strategic imperative for leaders around the world, whether they serve in Washington, run media companies or trading floors in New York City or guide tech titans in Silicon Valley.
While big data carries the baggage of huge hype, the institutions of federal government are getting serious about its genuine promise. On Thursday morning, the Obama Administration announced a “Big Data Research and Development Initiative,” with more than $200 million in new commitments. (See fact sheet provided by the White House Office of Science and technology policy at the bottom of this post.)
“In the same way that past Federal investments in information-technology R&D led to dramatic advances in supercomputing and the creation of the Internet, the initiative we are launching today promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security,” said Dr. John P. Holdren, Assistant to the President and Director of the White House Office of Science and Technology Policy, in a prepared statement.
The research and development effort will focus on advancing “state-of-the-art core technologies” need for big data, harnessing said technologies “to accelerate the pace of discovery in science and engineering, strengthen our national security, and transform teaching and learning,” and “expand the workforce needed to develop and use Big Data technologies.”
In other words, the nation’s major research institutions will focus on improving available technology to collect and use big data, apply them to science and national security, and look for ways to train more data scientists.
“IBM views Big Data as organizations’ most valuable natural resource, and the ability to use technology to understand it holds enormous promise for society at large,” said David McQueeney, vice president of software, IBM Research, in a statement. “The Administration’s work to advance research and funding of big data projects, in partnership with the private sector, will help federal agencies accelerate innovations in science, engineering, education, business and government.”
While $200 million dollars is a relatively small amount of funding, particularly in the context of the federal budget or as compared to investments that are (probably) being made by Google or other major tech players, specific support for training and subsequent application of big data within federal government is important and sorely needed. The job market for data scientists in the private sector is so hot that government may well need to build up its own internal expertise, much in the same way Living Social is training coders at the Hungry Academy.
“Big data is a big deal,” blogged Tom Kalil, deputy director for policy at White House OSTP, at the White House blog this morning.
We also want to challenge industry, research universities, and non-profits to join with the Administration to make the most of the opportunities created by Big Data. Clearly, the government can’t do this on its own. We need what the President calls an “all hands on deck” effort.
Some companies are already sponsoring Big Data-related competitions, and providing funding for university research. Universities are beginning to create new courses—and entire courses of study—to prepare the next generation of “data scientists.” Organizations like Data Without Borders are helping non-profits by providing pro bono data collection, analysis, and visualization. OSTP would be very interested in supporting the creation of a forum to highlight new public-private partnerships related to Big Data.
The White House is hosting a forum today in Washington to explore the challenges and opportunities of big data and discuss the investment. The event will be streamed online in live webcast from the headquarters of the AAAS in Washington, DC. I’ll be in attendance and sharing what I learn.
“Researchers in a growing number of fields are generating extremely large and complicated data sets, commonly referred to as ‘big data,'” reads the invitation to the event from the White House Office of Science and Technology Policy. “A wealth of information may be found within these sets, with enormous potential to shed light on some of the toughest and most pressing challenges facing the nation. To capitalize on this unprecedented opportunity — to extract insights, discover new patterns and make new connections across disciplines — we need better tools to access, store, search, visualize, and analyze these data.”
- John Holdren, Assistant to the President and Director, White House Office of Science and Technology Policy
- Subra Suresh, Director, National Science Foundation
- Francis Collins, Director, National Institutes of Health
- William Brinkman, Director, Department of Energy Office of Science
- Moderator: Steve Lohr, New York Times, author of “Big Data’s Impact in the World“
- Alex Szalay, Johns Hopkins University
- Lucila Ohno-Machado, UC San Diego
- Daphne Koller, Stanford
- James Manyika, McKinsey
What is big data?
Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.
The hot IT buzzword of 2012, big data has become viable as cost-effective approaches have emerged to tame the volume, velocity and variability of massive data. Within this data lie valuable patterns and information, previously hidden because of the amount of work required to extract them. To leading corporations, such as Walmart or Google, this power has been in reach for some time, but at fantastic cost. Today’s commodity hardware, cloud architectures and open source software bring big data processing into the reach of the less well-resourced. Big data processing is eminently feasible for even the small garage startups, who can cheaply rent server time in the cloud.
Teams of data scientists are increasingly leveraging a powerful, growing set of common tools, whether they’re employed by government technologists opening cities, developers driving a revolution in healthcare or hacks and hackers defining the practice of data journalism.
To learn more about the growing ecosystem of big data tools, watch my interview with Cloudera architect Doug Cutting, embedded below. @Cutting created Lucerne and led the Hadoop project at Yahoo before he joined Cloudera. Apache Hadoop is an open source framework that allows distributed applications based upon the MapReduce paradigm to run on immense clusters of commodity hardware, which in turn enables the processing of massive amounts of big data.
Details on the administration’s big data investments
A fact sheet released by the White House OSTP follows, verbatim:
“National Science Foundation and the National Institutes of Health – Core Techniques and Technologies for Advancing Big Data Science & Engineering
“Big Data” is a new joint solicitation supported by the National Science Foundation (NSF) and the National Institutes of Health (NIH) that will advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large and diverse data sets. This will accelerate scientific discovery and lead to new fields of inquiry that would otherwise not be possible. NIH is particularly interested in imaging, molecular, cellular, electrophysiological, chemical, behavioral, epidemiological, clinical, and other data sets related to health and disease.
National Science Foundation: In addition to funding the Big Data solicitation, and keeping with its focus on basic research, NSF is implementing a comprehensive, long-term strategy that includes new methods to derive knowledge from data; infrastructure to manage, curate, and serve data to communities; and new approaches to education and workforce development. Specifically, NSF is:
· Encouraging research universities to develop interdisciplinary graduate programs to prepare the next generation of data scientists and engineers;
· Funding a $10 million Expeditions in Computing project based at the University of California, Berkeley, that will integrate three powerful approaches for turning data into information – machine learning, cloud computing, and crowd sourcing;
· Providing the first round of grants to support “EarthCube” – a system that will allow geoscientists to access, analyze and share information about our planet;
Issuing a $2 million award for a research training group to support training for undergraduates to use graphical and visualization techniques for complex data.
Providing $1.4 million in support for a focused research group of statisticians and biologists to determine protein structures and biological pathways.
· Convening researchers across disciplines to determine how Big Data can transform teaching and learning.
Department of Defense – Data to Decisions: The Department of Defense (DoD) is “placing a big bet on big data” investing approximately $250 million annually (with $60 million available for new research projects) across the Military Departments in a series of programs that will:
*Harness and utilize massive data in new ways and bring together sensing, perception and decision support to make truly autonomous systems that can maneuver and make decisions on their own.
*Improve situational awareness to help warfighters and analysts and provide increased support to operations. The Department is seeking a 100-fold increase in the ability of analysts to extract information from texts in any language, and a similar increase in the number of objects, activities, and events that an analyst can observe.
To accelerate innovation in Big Data that meets these and other requirements, DoD will announce a series of open prize competitions over the next several months.
In addition, the Defense Advanced Research Projects Agency (DARPA) is beginning the XDATA program, which intends to invest approximately $25 million annually for four years to develop computational techniques and software tools for analyzing large volumes of data, both semi-structured (e.g., tabular, relational, categorical, meta-data) and unstructured (e.g., text documents, message traffic). Central challenges to be addressed include:
· Developing scalable algorithms for processing imperfect data in distributed data stores; and
· Creating effective human-computer interaction tools for facilitating rapidly customizable visual reasoning for diverse missions.
The XDATA program will support open source software toolkits to enable flexible software development for users to process large volumes of data in timelines commensurate with mission workflows of targeted defense applications.
National Institutes of Health – 1000 Genomes Project Data Available on Cloud: The National Institutes of Health is announcing that the world’s largest set of data on human genetic variation – produced by the international 1000 Genomes Project – is now freely available on the Amazon Web Services (AWS) cloud. At 200 terabytes – the equivalent of 16 million file cabinets filled with text, or more than 30,000 standard DVDs – the current 1000 Genomes Project data set is a prime example of big data, where data sets become so massive that few researchers have the computing power to make best use of them. AWS is storing the 1000 Genomes Project as a publically available data set for free and researchers only will pay for the computing services that they use.
Department of Energy – Scientific Discovery Through Advanced Computing: The Department of Energy will provide $25 million in funding to establish the Scalable Data Management, Analysis and Visualization (SDAV) Institute. Led by the Energy Department’s Lawrence Berkeley National Laboratory, the SDAV Institute will bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department’s supercomputers, which will further streamline the processes that lead to discoveries made by scientists using the Department’s research facilities. The need for these new tools has grown as the simulations running on the Department’s supercomputers have increased in size and complexity.
US Geological Survey – Big Data for Earth System Science: USGS is announcing the latest awardees for grants it issues through its John Wesley Powell Center for Analysis and Synthesis. The Center catalyzes innovative thinking in Earth system science by providing scientists a place and time for in-depth analysis, state-of-the-art computing capabilities, and collaborative tools invaluable for making sense of huge data sets. These Big Data projects will improve our understanding of issues such as species response to climate change, earthquake recurrence rates, and the next generation of ecological indicators.”
Further details about each department’s or agency’s commitments can be found at the following websites by 2 pm today:
IBM infographic on big data
This post and headline have been updated as more information on the big data R&D initiative became available.
The future of cities was a hot topic this year at the SXSW Interactive Festival in Austin, Texas, with two different panels devoted to thinking about what’s next. I moderated one of them, on “shaping cities with mobile data.” Megan Schumann, a consultant at Deloitte, was present at both sessions and storified them. Her curatorial should gives you a sense of the zeitgeist of ideas shared.
We are deluged in big data. We have become more adept, however, at collecting it than in making sense of it. The companies, individuals and governments that become the most adept at data analysis are doing more than find the signal in the noise: they are creating a strategic capability. Why?
“After Eisenhower, you couldn’t win an election without radio.
After JFK, you couldn’t win an election without television.
After Obama, you couldn’t win an election without social networking.
I predict that in 2012, you won’t be able to win an election without big data.”
In November 2012, we’ll know if his prediction came true.
All this week, I’ll be reporting from Santa Clara at the so-called “data Woodstock” that is the Strata Conference. Croll is its co-chair. You can tune in to the O’Reilly Media livestream for the conference keynotes.
For some perspective on big data and analytics in government, watch IBM’s Dave McQueeney at last year’s Gov 2.0 Summit:
Or watch how Hans Rosling makes big data dance in this TED Talk: