http://gigaom.com/2010/05/13/microsoft-wants-to-build-its-business-with-data/ Sat, 25 May 2013 23:54:36 +0000 hourly 1 http://wordpress.com/ http://gigaom.com/2010/05/13/microsoft-wants-to-build-its-business-with-data/#comment-490536 Thu, 28 Oct 2010 20:39:50 +0000 http://gigaom.com/?p=119823#comment-490536 [...] new features don’t stop there. Microsoft also brought its application Marketplace and DataMarket (codenamed Dallas) out of beta; unveiled the Windows Azure Virtual Network portfolio; added numerous features to [...]

]]> http://gigaom.com/2010/05/13/microsoft-wants-to-build-its-business-with-data/#comment-251280 Tue, 25 May 2010 17:18:38 +0000 http://gigaom.com/?p=119823#comment-251280 [...] data: Ookla hopes to provide the information for free to academic researchers, but it also plans to charge ISPs, analysts and governments for it. Ookla has no debt or venture capital, and is [...]

]]> http://gigaom.com/2010/05/13/microsoft-wants-to-build-its-business-with-data/#comment-251279 Tue, 25 May 2010 12:02:11 +0000 http://gigaom.com/?p=119823#comment-251279 [...] data. Ookla hopes to provide the information for free to academic researchers, but it also plans to charge ISPs, analysts and governments for it. Ookla has no debt or venture capital, and is [...]

]]> http://gigaom.com/2010/05/13/microsoft-wants-to-build-its-business-with-data/#comment-251278 Fri, 14 May 2010 14:49:19 +0000 http://gigaom.com/?p=119823#comment-251278 Stacey, your post and report provide a very timely and useful discussion of important developments. As you point out, through AWS and other sources, the tools and infrastructure with which to capture and analyze Big Data are now available to virtually anyone, not just the Googles and Yahoos of the world. Still, I would estimate that 99.9% of the data generated go out the “data exhaust” and are not currently being examined, much less mined at a granular level. The emergence of a “big data marketplace” creates new opportunities for data owners and developers alike. In addition to privacy and other issues you note, two others need to be considered: (i) data, particularly from disparate sources (as is typical in mashups), are often “messy” and not easily linked – consider, for instance, the many different ways in which location can and is represented. As a result, disambiguating and joining data at the entity level from disparate datasets are critical tasks – however, few “Big Data” solutions are very good at either; (ii) latency – like wine, data age. Unlike wine, it may be important to continuously compare the “newest” data with historical data, particularly if the objective is to detect and respond in near real-time. Organizations and users should carefully consider both the quality and currency of the data as well as the ability of the tool(s) they’re using to accommodate and disambiguate data from disparate sources. Some of these issues will be addressed in the session on Big Data that I’m moderating at the upcoming GigaOm Structure Conference (June 23-24; http://events.gigaom.com/structure/10/).
Dr. Phil Hendrix, immr

]]>