Big Data. Big Decisions
InformationWeek
Special Coverage Series


Government Braces For Big Data

Terabytes are growing into petabytes. Your action plan must include new skills, tools and cross-agency collaboration.

InformationWeek Green - Feb. 11, 2013
InformationWeek Green
Download the entire February 2013 issue of InformationWeek Government, distributed in an all-digital format as part of our Green Initiative
(Registration required.)


Big Data

Petabytes of information are accumulating across government: military veterans' genomics data, climate records dating to the 16th century, years worth of stock trades and even the results of particle physics experiments.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

The era of big data has arrived in government just as it has in business. Digital documents, transactions, intelligence, photos, video, Web content and electronic correspondence are filling storage systems to the brim. At the same time, IT budgets are flat, agencies are being pressed to consolidate data centers, and IT teams don't have the skills they need to manage, analyze and apply all of that data.

Government CIOs and their staffs must quickly work their way up the big data learning curve. Terabyte databases are growing into petabyte databases, pushing the processing and storage limits of the IT systems in place and testing the know-how of even the most experienced database managers.

A big data workshop held by the National Institute of Standards and Technology in January drew more than 800 attendees from federal agencies and the technology companies that work with them. IT leaders from the Department of Defense, the Department of Energy, NASA, the National Oceanic and Atmospheric Administration (NOAA), Veterans Affairs and the White House were among those who came to discuss the convergence of big data and cloud computing, big data life cycle management and big data analytics.

The Obama administration pushed big data up the federal IT priority list last March when it unveiled a formal research and development initiative aimed at developing new technologies for big data management and analysis. The goal is spurring breakthroughs in science and engineering, transforming education and strengthening national security. In a blog post titled "Big Data Is A Big Deal," Tom Kalil, deputy director for policy at the Office of Science and Technology Policy, called for an "all hands on deck" among government, businesses, universities and nonprofits.

To kick-start the effort, six federal agencies -- the Defense Advanced Research Projects Agency, the departments of Defense and Energy, National Institutes of Health (NIH), National Science Foundation (NSF), and U.S. Geological Survey (USGS) -- announced plans to invest $200 million collectively in big data R&D. A new interagency steering group is crafting a national R&D strategy, the components of which include foundational research, development of IT infrastructure that's "big data ready," education and workforce development, and collaboration.

Some agencies have begun to develop their own plans for big data research and management. The Pentagon will spend $250 million annually on big data ($60 million of which is included in the $200 million in new federal research). One area of investment is a DARPA program called XDATA to develop "computational techniques and software tools for sifting through large structured and unstructured data sets," according to a White House document on the federal initiative.

To read the rest of the article,
Download the February 2013 issue of InformationWeek Government



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.