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Auxiliary Vs for Big Data

Big Data has moved to the “Trough of Disillusionment” phase in the “Hype Cycle for Emerging Technologies” by Gartner released last month. Also, Data Science was added in the chart, bordering between the “Innovation Trigger” and “Peak of Inflated Expectations” stage. Despite the first-time appearance in the cycle, Data Science has a long history of evolution, arguably for more than 50 years. For example, the “Data Science Journal” and “Journal of Data Science” were launched over 10 years ago. It seems to me that a better term for this era is “Big Data Science” for the discipline expanded and applied in the Big Data space. To that, I’d have 3 auxiliary V-words for Big Data:
 
  • Verbiage: There are a variety of manners or styles of expressing something in words. It is hard to analyze the unstructured data like tweets and blogs in the traditional way. Big Data solutions provide better lexical analysis techniques and tools to efficiently find useful information in a massive quantity of data, e.g. hot topics, likes, sentiments, trending, categorization, relation, clustering, patterns, etc.
  • Voice: Voice-driven commands and searches are becoming norm, like Siri and Google Now. Speech recognition and interpretation are used in Watson, which beat the top champions in Jeopardy. Audio mining and speech analytics help analyze the customers in real time or recording to gather information, improve client interactions, and extract critical business intelligence buried in the call center. It is leveraged to identify unsatisfied customers or users in frustration, and also help allocate right-level resources with appropriate training or coaching.
  • Visual: Big Data deals with data types beyond the traditional text, such as image and video data. Faces can be detected in photos and tagged to sort and group pictures. Image search engines are also available to find photos online. Big Data enables more effective video surveillance and tracking. Facial recognition systems are used in airports to monitor passing individuals, match passport holders, and run fully automated border controls.
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Tony Shan works as a senior consultant, advisor at a global applications and infrastructure solutions firm helping clients realize the greatest value from their IT. Shan is a renowned thought leader and technology visionary with a number of years of field experience and guru-level expertise on cloud computing, Big Data, Hadoop, NoSQL, social, mobile, SOA, BI, technology strategy, IT roadmapping, systems design, architecture engineering, portfolio rationalization, product development, asset management, strategic planning, process standardization, and Web 2.0. He has directed the lifecycle R&D and buildout of large-scale award-winning distributed systems on diverse platforms in Fortune 100 companies and public sector like IBM, Bank of America, Wells Fargo, Cisco, Honeywell, Abbott, etc.

Shan is an inventive expert with a proven track record of influential innovations such as Cloud Engineering. He has authored dozens of top-notch technical papers on next-generation technologies and over ten books that won multiple awards. He is a frequent keynote speaker and Chair/Panel/Advisor/Judge/Organizing Committee in prominent conferences/workshops, an editor/editorial advisory board member of IT research journals/books, and a founder of several user groups, forums, and centers of excellence (CoE).