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Krish Krishnan

"If we knew what it was we were doing, it would not be called research, would it?" - Albert Einstein.

Hello, and welcome to my blog.

I would like to use this blog to have constructive communication and exchanges of ideas in the business intelligence community on topics from data warehousing to SOA to governance, and all the topics in the umbrella of these subjects.

To maximize this blog's value, it must be an interactive venue. This means your input is vital to the blog's success. All that I ask from this audience is to treat everybody in this blog community and the blog itself with respect.

So let's start blogging and share our ideas, opinions, perspectives and keep the creative juices flowing!

About the author >

Krish Krishnan is a worldwide-recognized expert in the strategy, architecture, and implementation of high-performance data warehousing solutions and big data. He is a visionary data warehouse thought leader and is ranked as one of the top data warehouse consultants in the world. As an independent analyst, Krish regularly speaks at leading industry conferences and user groups. He has written prolifically in trade publications and eBooks, contributing over 150 articles, viewpoints, and case studies on big data, business intelligence, data warehousing, data warehouse appliances, and high-performance architectures. He co-authored Building the Unstructured Data Warehouse with Bill Inmon in 2011, and Morgan Kaufmann will publish his first independent writing project, Data Warehousing in the Age of Big Data, in August 2013.

With over 21 years of professional experience, Krish has solved complex solution architecture problems for global Fortune 1000 clients, and has designed and tuned some of the world’s largest data warehouses and business intelligence platforms. He is currently promoting the next generation of data warehousing, focusing on big data, semantic technologies, crowdsourcing, analytics, and platform engineering.

Krish is the president of Sixth Sense Advisors Inc., a Chicago-based company providing independent analyst, management consulting, strategy and innovation advisory and technology consulting services in big data, data warehousing, and business intelligence. He serves as a technology advisor to several companies, and is actively sought after by investors to assess startup companies in data management and associated emerging technology areas. He publishes with the BeyeNETWORK.com where he leads the Data Warehouse Appliances and Architecture Expert Channel.

Editor's Note: More articles and resources are available in Krish's BeyeNETWORK Expert Channel. Be sure to visit today!

In the blur of Big Data, there is a element of suspense and mystery that prevents one from adopting to the same, what information is available and where to find integration points for linking the same to your enterprise. While there are several technologies available to address the volumetric's problem, there is one way to address the complexity and ambiguity side of Big Dat, using Taxonomies to create a Data Discovery exercise.

Taxonomies have long been used as catalog or index creation mechanisms in the world of metadata driven approach to data management and more so in the Web driven architecture where you need linked context behind the scenes. The very same taxonomy family can simply be used to create what we call word clouds or tags from content that is within Big Data. these tags can be used to create powerful linkages that will form a lineage and a graph.

What about Data Quality? that is the biggest advantage of using Taxonomies. When you have spelling errors and language issues, due to the intrinsic nature of taxonomies, you can land to a margin of error equation and often arrive at a close match.

Will this work on all types of big data, from my experiments and learning's it has worked with almost all types of data that can be deciphered by human minds. My next article in this channel will be focusing on this subject.

What can you do with the output from such a discovery? the obvious answer is that you can create a data road-map with linkages to all data across the enterprise. This is a foundational first step in a bigdata journey.

Posted May 17, 2012 12:51 PM
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