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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!

January 2013 Archives

If you read books on strategy and management as subjects, the end rule always focuses on the term ROI and the time to realize the same. There is nothing wrong in estimating the value of any investment, the market opportunity, risks and time to recover profits, but the situation changes drastically when you apply the same techniques to an organization on all the programs and projects in-house, often resulting in chaotic situations and emotional upheavals.

The main idea behind applying ROI based techniques for any in-house program is to provide management with a roadmap on the value of the investments in technology and the business benefit it will bring about. The one area where we have traditionally struggled to provide a clear and concise point of view is the area of "data", ironically while the same has been classified and touted as an enterprise asset for many years. How does one really apply ROI to data strategy?

The techniques of using data quality and integrated data architectures help in building a business case for data strategy, but does not clearly articulate the ROI as it does not tie the business outcomes to the data strategies used in the organization. In order to measure the ROI on data strategy, we need to employ a combination of
 
  • projected or predicted ROI from all data programs
  • realized increase in business initiatives
  • increase or decrease in profitability
  • measured customer sentiment

By creating a mashup  of the different pieces, we can create a co-relation on the initiatives of data strategy and the ROI, with a measurement of true impact on the business. This type of value driven mechanism is needed to realize the true ROI on data strategy.

Monetization from the data strategy efforts can be traced with this method and clearly documented along with trends and timelines. This is not a simple exercise and needs to be implemented with acceptable margin of error for the first few iterations till a maturity model can be established.

Once we have this type of a model, every program of data strategy can be tied to a measurable value and you can predict the tangible ROI and the timeline for the realization with a higher degree of confidence. This type of practice exists in many organizations albeit on a tribal scale and it needs to be enabled and empowered to become an enterprise level strategy.

This type of approach uses all the soft costs and the tangible performance results of the business together and hence it is to be treated with utmost security and governance to protect the competitive advantage of the business. Watch for my expanded whitepaper in the next week on this topic.


Posted January 30, 2013 7:56 AM
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