by clicking on the page. A slider will appear, allowing you to adjust your zoom level. Return to the original size by clicking on the page again.
the page around when zoomed in by dragging it.
the zoom using the slider on the top right.
by clicking on the zoomed-in page.
by entering text in the search field and click on "In This Issue" or "All Issues" to search the current issue or the archive of back issues respectively.
by clicking on thumbnails to select pages, and then press the print button.
this publication and page.
displays a table of sections with thumbnails and descriptions.
displays thumbnails of every page in the issue. Click on a page to jump.
allows you to browse through every available issue.
FCW : April 30, 2015
20 April 30, 2015 FCW.COM Big data the system accommodates their work processes. In addition to prosecutions and sniffing out suspicious payments before they’re made, CMS also uses its data to block potentially problematic providers from enrolling and to revoke some entities’ status as Medicare providers if they’ve been identified as having billed the government inappropriately on multiple occasions. As a result, the Department of Health and Human Ser- vices reported in March that its fraud-prevention efforts had contributed significantly to the recovery of $3.3 billion for taxpayers in fiscal 2014. Improving access to data Of course, for some agencies, big data is nothing new. The Federal Emergency Management Agency, for example, built a prototype of a data warehouse business intelligence tool 15 years ago, said Mark DeRosa, director of business intel- ligence and analytics at Definitive Logic, a management consulting and systems engineering firm. FEMA has been using the cost/benefit analysis tool to analyze the vast streams of data pouring into the agency and help it quantify the relative value of engaging in vari- ous disaster-mitigation projects. The goal is to prioritize projects that would provide taxpayers with a maximum return on investment. However, DeRosa said many agencies still live in a world of multiple, unconnected databases, which makes it dif- ficult — if not impossible — for employees to access the data to derive actionable knowledge. “We have to recognize that the average user of these systems is not a techie,” DeRosa said. “We can’t expect them to write code to access their data. What we need to do is to put together solutions that give users the ability to integrate raw data and to transform it into information. What I mean is applying the business rules and logic to that data and converting it into something that the average The quietest whispers from the Federal Reserve Board can move markets worldwide, so the data the central bank of the United States uses to model its economic projec- tions has to set the standard for reli- ability and credibility. The Fed also releases its own set of economic indicators based on data it collects from financial institutions and established finan- cial information suppliers such as Bloomberg and Reuters. But the Fed is also beginning to reckon with the explosion of com- mercial, financial, consumer and other data that has been taking place in the past decade. Although there are risks, practical problems and institutional obstacles to incorporat- ing that range of data into the Fed’s economic modeling, there are oppor- tunities as well, said Micheline Casey, the Fed’s chief data officer. In her nearly two years on the job, Casey has been building a data organization inside the Fed that has an annual budget of about $12 mil- lion and employs about 40 people — and growing. She’s starting to think about how the oceans of data generated by e-commerce firms, real estate transaction sites, crowd- sourced gas price tools, and even sensor data from roads and mass transit systems can provide the Fed’s policymakers and economists with reliable information. For example, data from Amazon and Walmart could supply up-to- date consumer price information; real estate bidding and closing information from Trulia and Zillow could send signals about slack or pent-up demand in the market; data on 3D printing could augur shifts in manufacturing; data on the use of emerging companies in the sharing economy such as Uber and AirBNB could point to changes in car own- ership levels or hotel occupancy, respectively. Collectively, those and other economic signals buried in silos of proprietary data could provide a “Fitband for the economy,” giving a near-real-time impression of the macroeconomic health of the U.S. and the world, Casey said. “We’re always looking to improve our forecasting and understand what’s really going on in the economy and what will happen tomorrow,” she said in a March 31 keynote speech at the annual Enter- prise Data World conference. “What we’ve been trying to do over the past several years, as the explosion of data has become much more prevalent, is to move forward and stop driving by looking in the rear- view mirror and start identifying Federal Reserve banking on new data sources 0430fcw_016-021.indd 20 4/8/15 2:35 PM
April 15, 2015
May 15, 2015