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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!

June 2012 Archives

In a recent series of conversations that occurred with CxO's, a common lament that was expressed is the lack of Actionable or Action Oriented insights from Analytics or Business Intelligence. Upon deeper analysis of this lament, what I have seen is there is more of the "latency curve" that is building silently after the initial metrics and analytics are delivered for decision support.

We initially discovered that on a time value curve, the data acquisition, analysis and delivery latencies, which were built due to design and architecture weaknesses. With the advent of technology and tools, we have somewhat overcome the initial latencies deficiency. With the reduction of initial latency, we have enabled adoption of Business Intelligence. But the adoption has brought the second problem, the situation is something similar to the sequence below

  1. Company XYZ implements a DWBI solution
  2. Users develop reports and analytics,  discover performance needs
  3. Performance and Architecture are tuned
  4. Users start gaining adoption to the Analytics and Reports
  5. Executive Dashboards are commissioned and provide critical metrics
  6. An executive asks a simple question like - How or Why or What? - this is where we build the secondary latencies

The post query analysis to provide different levels of data to the executive decision maker is where the effective use of Business Intelligence falls off the cliff. The different reports, data points and metrics quickly get transitioned from a BI tool to Excel spreadsheets and further analysis and discovery leads to chaos and eventually a million dollar investment loses steam.

This is where we need to draw our attention to looking at new data visualization techniques like mashup's. Powerful tools like Tableau, Spotfire and BIS2 offer a wide variety of data mashup techniques. With a mashup, you can create a summary and details views separately and present them in one single view. This will be an easier technique as you will think of the scenarios together and present better analytics as a end result. There are better techniques then drill down and drill across with the advent of newer visualization tools, as related to providing actionable insights and analytics. This will reduce post analysis latencies and drive Business Intelligence success.

Posted June 14, 2012 9:47 AM
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While we all agree about the importance of Big Data, it is a common struggle to develop a business case based on potential value from implementing a Big Data solution with your Data Warehouse or Business Intelligence. In most cases the classic IT vs Business rears its head and in settling down on a agreed approach, valuable opportunity is lost. In these types of situations is where a sandbox will help. Whether it is available on a platform that is currently used, is a virtualized environment or will be implemented as a silo solution, using a sandbox organizations can solve a number of "entry barriers" like technology choices, data quality, security and often establish standards for a deeper and larger integration.

With the advent of Analytics and Big Data, the need for Sandbox environments is going to increase dramatically. If you are a SMB or MMB sized business, you might be tempted to go the cloud route and that might be perfectly okay. But large enterprises cannot simply move to a cloud footprint for this exercise and private clouds and virtualization play a vital role in these situations.

The users of this type of environment will be Business SME's and Data Scientists, both of these user types need an environment where they can play without limitations. Unleash the creative potential and save valuable time by adopting to a practice of Sandbox. Many successful teams that I know are reaping rich benefits from this environment. While you may not solve a mega problem, you will definitely not create a mega problem by keeping all your development and QA environments clean of the exercises conducted on Sandboxes.

Posted June 1, 2012 9:06 AM
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