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FCW : October 30, 2013
Dee e A hile business intelligence tools and enterprise data warehouses have long helped agencies understand more about their operations, they can only go so deep into the data. Business intelligence software, for example, works best on structured data --- not the unstructured data that is growing so rapidly --- and is limited in the amount of data it can handle. between different types of data, agencies are turning to predictive analytics. These tools help organizations uncover patterns from large amounts of both structured and unstructured data --- patterns that not only future occurrences. While predictive analytics is an umbrella term, there are many types of analytics tools and malicious intent. These include: Social media analytics: Collects and analyzes intelligence gathered from a variety of social media sources Text mining: Provides deep analysis of text-based documents. Sentiment analysis: Analyzes electronic e-mail and more to identify a user's thinking and priorities. Geospatial analysis: Analyzes data from satellites, global navigation systems, aerial surveys, sensor networks and radar. Whether your mission is to increase terrorists, predictive analytics is an effective way of making sense of big data. Here are a few examples: Anti-money laundering: Predictive account activity. These patterns help identify suspicious activity and can activities are likely to occur. Law enforcement: Agencies can us predictive analytics to analyze terabytes of travel and immigration data, motor vehicle registration information and other data to track fraud, stolen property, narcotics Insider threats: By analyzing patterns in employee usage of technology, electronic data, predictive analytics can uncover patterns and anomalous behavior that lead to capture. No matter the goal, predictive analytics is an effective tool in the big data arsenal. With the right tools, agencies can further investigation, prevent events from escalating or even stop events before they occur. Game GAME CHANGING ECHNOLOG O MEE AGENC MI ION SPONSORED REPORT DEEP ANALYTICS HADOOP AN O NER'S GUIDE here is a reason why the most e ective analytics tools today are based on Hadoop --- it was tailor-made to handle the requirements of massive amounts of unstructured data. Originally developed by data scientists at ahoo and Google, Hadoop today is available either as a distribution on which to build applications (from Apache, Cloudera, IBM and others), or as part of an analytics tool. It's the perfect complement to big data, because it is designed to process, store and analyze petabytes and exabytes of distributed, unstructured and structured data. And it works: According to Ventana Research, 94 percent of Hadoop users perform analytics on large volumes of data not previously feasible. Eighty-eight percent analyze data in greater detail, while 82 percent can now retain more of their data. Here's how it works, in a nutshell. Hadoop breaks the data into pieces and stores them in the Hadoop Distributed File ystem (HDF ), which can scale to hundreds of nodes on a single cluster and support tens of millions of files in a single instance. It is then ready to be analyzed by the MapReduce framework. Because the data has been broken into pieces, each part can be analyzed at the same time, improving e ciency. Hadoop is a cost-e ective and scalable method of storing, manipulating and querying data, making it an ideal platform for analysis. At the same time, it is a young, somewhat complex technology that requires deep knowledge for maximum benefit. hat's why many organizations use Hadoop --- sometimes unknowingly, in fact --- in business intelligence, analytics or E L (Extract/ ransform/Load) tools. With these tools and the capabilities that Hadoop enables, it's possible to make great leaps in the types of analysis that are possible. For example, by combining structured and unstructured data analysis, agencies can glean unique insights that may previously have escaped attention. BY THE NUMBERS BIG DATA IN GOVERNMENT 4 he amount in billions that federal government spent on vendor-supplied big data solutions in 2012. 6 he percentage that structured and unstructured data is growing per year in health care 7 2 he amount in billions that federal government will spend on vendor-supplied big data solutions in 2017. 24 he amount in millions NIH plans to spend per year for the next four years on big data in biomedicine. 150 he number of exabytes of U. . healthcare data in 2009 (1 exabyte = more than 1 million gigabytes). 200+ he amount in millions that the Obama administration allocated to the Big Data Research and Development Initiative in March 2012. 235 he number of terabytes of data that has been collected by the Library of Congress as of April 2011. 250 he amount in millions that the Pentagon plans to spend annually on big data. 4 he amount of data the average state or local agency stores, in terabytes. 848 he number of petabytes of data the U. . government produced in 2009.
September 30, 2013
November 15, 2013