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Hadooplicability

The adoption of Hadoop has been increasing. More and more organizations are using Hadoop for various solutions. Some companies replace the existing data stores with Hive and HBase. Some firms make use of Mahout for machine learning. Others are building new applications on the Hadoop platform from the ground up. Now the critical question is where Hadoop can and should be used. 

Hadooplicability is the measure of the Hadoop applicability. It helps users find the most applicable areas where Hadoop can be leveraged. While Hadoopability, as defined before in another post of this blog, deals with the suitability primarily from a technology standpoint, Hadooplicability is more about the business case for Hadoop from a user perspective. Apparently there are multiple ways to assess and articulate why Hadoop is good for business, such as drivers, imperatives, benefits, impacts and cost. To make it simple, I classify the major usage scenarios and use cases into 3 categories: Bad, Innovative and Good (BIG).
  • Find bad stuff
    • Identify threats and frauds to reduce risks and minimize loss.
    • Analyze complaints and unsatisfactory comments posted on social networks.
    • E.g. fraud detection in insurance claims; trade surveillance for illegal transactions; customer churn analysis in telco to retain customers; discover fraudulent activities in anti-money laundry; spam filtering.
  • Strengthen good things
    • Understand customers in more depth to serve better.
    • Study the user behaviors to improve the services and CRM.
    • E.g. conduct sentiment analysis; examine spending habits and preferences; inspect the search attempts and clickthroughs to enhance search quality, ranking, display order and relevance.
  • Drive innovative advancement
    • Take proactive measures to gain competitive advantages.
    • Boost the user experience at the individual level.
    • E.g. ad targeting in one-to-one marketing; recommendations on shopping items and travel plans; pattern finding in Big Data mining; point-of sale transaction analysis to correlate multiple factors like weather, local news, major events, etc.

In a nutshell, Hadooplicability helps group applicable uses of Hadoop in a structured manner. Subcategories are further created for different usage patterns to deal with more granular use cases. 

For more information, please contact Tony Shan (blog@tonyshan.com). ©Tony Shan. All rights reserved.

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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).