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AlanMorrison
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Hi, Bill. I'm a bit weary of what seems to be whining and just plain ignorance on the part of the BI community that seems evident in these survey results. Many BI analysts are unaware of more modern, open source, content-centric integration and analytic approaches that scale better than ETL + data warehousing and deal with less structured data. For example: (1) Standardized graph data stores (RDF or comparable triple or quad stores) for Web-scale integration: Graphs are more articulated and much easier to join than tabular, relational databases. Some vendors like InSilico Discovery now serve as on-the-fly report integration SaaSes for banks. Data description via inferencing and ontologies scales, as ISD and others have proven. The Semantic Web stack (RDF/RDFS/OWL) is in use at numerous media companies such as the BBC, NYT, Reuters, Wolters Kluwer, Lexis-Nexis--i.e., content companies. Lately, software vendors like Cisco and Amdocs are basing their products on these triple stores for scalability reasons. Many BI specialists just haven't worked much with content or are averse to trying a method that initially seems alien to them. See http://www.pwc.com/us/en/technology-forecast/spring2009/semantic-web-technologies.jhtml and my Sem Web Quora answers at http://www.quora.com/Alan-Morrison/Semantic-Web/answers for more detail. (2) Parallel processing a la Hadoop (derived from the Google Cluster Architecture, Bigtable and MapReduce) or its NoSQL cousins: This method speeds up high-volume data crunching and makes it cost effective. Companies like Backtype (bought by Twitter)and FlightCaster have been analyzing scads of Web data on the cheap, and started just with a handful of staff and EC2 clusters. Others like Disney just kept servers they were going to retire and with the help of a few savvy staffers made them into Hadoop clusters. See http://www.pwc.com/us/en/technology-forecast/2010/issue3/features/big-data-pg1.jhtml for more detail. In other words, the large-scale methods are out there, but just aren't evenly distributed. Shades of George Box.... There are ways to do large scale integration high-volume, fast crunching of less-structured data, and companies like Google and the BBC have paved the way. Other companies just need to pay attention. Social information will actually help machines make the connections, but data in graph form is what will enable sufficiently context-rich, large-scale integration. A brief animation at http://www.pwc.com/us/en/technology-forecast/2011/issue3/index.jhtml explains the phenomenon. We also interviewed your pal Sameer Patel in this issue of our journal. Hope this helps for background, and that your painting is going well.... @AlanMorrison
I agree, John, with you that Gladwell shouldn't be shooting the messenger. 20 percent of the people provide 80 percent of the value in social activism (or anything else). How do you find and tap those 20 percent? You use the range of communications channels available to you. Social media provides another channel, one with its own pros and cons. On the pro side, it's a many-to-many paradigm without geographical boundaries, which is in some ways complementary to the the many-to-many local organization of the kind that the Greensboro activists exemplify. You could easily see how social media could expand the activist footprint if approached strategically. On the con side, there's a lot of noise versus signal. Some people have the patience for a noisy channel. Others (apparently Gladwell is among them) don't.
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Oct 1, 2010