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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!

An intriguing bemoan from most users that I have heard is "what is the value of my data?". Well if you are looking for $$s and cents, then your data has value from the time it is created till the next few hours where it gets analyzed, after that it is history and considered as dead weight in strict terms. However being born collectors, human tendency is to keep all this weight around and get a very obese and unhealthy data collection, that will eventually just die due to its sheer volume(read weight).

The value of your data is measured in different ways
  • Origination - This is a point of creation of the data, can be a transaction, an email, an application for insurance or a claim. Data is deemed to be often dirty at this juncture and data quality rules are applied for correction. This is the point in time where data has the highest value
  • Transformation - This is a point of collecting and transforming the data to be ingested into analytical and reporting platforms. At this point again due to the number of rules that are applied, data here will have a very high value
  • Analysis and Reporting - This is the last point in the life-cycle where the data value is held high. The data here points to trends and behaviors as simple metrics, but yet will serve a very useful purpose of being a treasured indicator

There are a number of Data Quality indicators that will be able to measure the effectiveness of quality across the enterprise both in origination and transformation phase. These indicators will be a very useful point to prove that data of good quality has a high value as it helps speed up decision support platforms. This is one way to assess and prove the value of your data.

The second way to assess and prove the value of data, is to measure its effectiveness when used as metrics and KPI's in reports and Analytics. The quality and timeliness of data here will be measurable with future results which can be compared with current results, and the differential lift can be attributed to the value of data being available, with the right quality and at the right time.

While none of these are new techniques, with the same question arising many times, it is prudent to nudge the good old ways and ensure that the simplicity of using these can be the best innovations that you have accomplished in assessing the value of your data.

Posted December 2, 2011 6:00 AM
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