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.
GCN : August 2014
WHEN A NEW, powerful tool comes along, there s a tendency to think it can solve more problems than it actu- ally can. Computers have not made o ces paperless, for example, and Preda- tor drones haven t made a significant dent in the annual number of terrorist acts. To judge by the number of requests for proposals coming out of agencies and departments, the current power tool of choice is one that can apply analytic tools to massive amounts of data to find underlying, meaning- ful patterns. Government agencies, for example, are using big data tools to detect crime. The Department of Homeland Security is using big data tools to scan social media for signs of terrorists, and private companies are using similar tools to detect insider threats. While big data tools are, indeed, very powerful, the results they deliver tend to be only as good as the strate- gy behind their deployment. A closer look at successful big data projects o ers clues as to why they are successful ... and why others fall short of the mark. BIG DATA ON THE GROUND One of the most e ective big data projects I ve covered in recent years is the USDA Risk Management Agency s pro- gram to set crop insurance rates and to detect fraudu- lent claims of crop losses. The agency starts with FCI- 33 rate maps created in ESRI ArcView that combine data from a variety of sources, including soil data from the Natural Resources Conserva- tion Service, farm location and crop data from the Farm Services Agency, historic weather data and satellite imagery. After assessing this data, agency analysts create zones that establish crop insurance rates for each parcel of land. The bonus is that when claims are made, the same system can be used to detect and investigate potentially fraudulent claims. The RMA project has the characteristics that mark a solid big data project: A mea- surable, defined goal and the use of data that is measur- able and clearly relevant to the goal. So what happens if you take the tool and apply it to another task, say, analyz- ing patterns of student loan default rates? The first step is to figure out what data to collect that is relevant: student age, gender and socio-economic indicators; type of school (public, pri- vate, for-profit); geographic location. The tool kicks out a result: student loan default rates are higher at for-profit schools than at other schools. But why? Does that mean those colleges aren t doing a good job of preparing students for jobs? Or does it mean less- qualified students are going to those schools because they can t get into more com- petitive, public universities? Or does it mean for-profit schools are training students for jobs that are lower paying or that are in sectors that aren t hiring? CAN DATA REVEAL INTENT? Big data tools get even more problematic when they are applied to such goals as fer- reting out insider threats or terrorist activity. When the Department of Homeland Se- curity scans social media for people talking about bombs and using racist or intoler- ant language, how does it distinguish between people who are likely to actually take action versus those who are just venting? Surely there are many more who fall in the latter category. How e ective is such a big data tool if it sends agents out on wild goose chases? What s more, even if the sentiment analysis tools used by DHS are accurate enough to distinguish between some- one who is a real threat and someone who is just venting or writing fiction, analysts need to consider the impact of the tool itself on what is being studied. While the RMA s project doesn t a ect the weather or soil characteristics, it can a ect the behavior of people who may file false crop dam- age claims. In short, the key to a suc- cessful big data project isn t the bigness of the data or the slickness of the dashboard a given tool provides. It s the quality of the selection and analysis of the data. Unfortunately, in many cases, those who use the big data tools may not even be aware of the underlying logic of the data selection and analysis. • The hidden dangers of big data tools BY PATRICK MARSHALL EMERGING TECH 34 GCN AUGUST 2014 • GCN.COM The key to a successful big data project isn't the bigness of the data or the slickness of the dashboard, it's the quality of selection and analysis of the data.