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Focus on Analytics: HP Global Analytics is equal to the challenges of Big Data

Featuring: Rohit Tandon, Vide President, HP Global Analytics


LINK to article: HP Global Analytics is equal to the challenges of big data


Rohit recently contributed an article in the latest Edition of HP’s BPO Edge e-zine titled, “HP Global Analytics is equal to the challenges of big data”. 

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In this article Rohit looks at how combining the vast amounts of semi-structured and unstructured data that is now available with the traditional structured operational data can provide context, thereby delivering actionable insights to increase a company’s competitive advantage.


Rohit explains how HP Global Analytics team has leveraged it’s year of experience to bring analytics to life and that there are four layers in a comprehensive solution that need to work in sync to make this happen: data sourcing, data storage and querying, analytical modeling and visualization.


Rohit also provides some details around how HP Global Analytics are utilising HP Autonomy’s information access platform, Autonomy IDOL 10, to address the challenges of real-time data sourcing.


Please take a moment to read this very important and insightful article.


LINK to article: HP Global Analytics is equal to the challenges of big data


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About Rohit Tandon


Rohit Tandon is the Vice President and World Wide Head – Global Analytics at Hewlett-Packard Company and leads HP’s Rohit Tandon.jpganalytics organization. As head of Global Analytics, Rohit is helping drive the Analytics ecosystem to support HP’s vision and priorities, through delivery of cutting edge analytical capabilities across sales, marketing, supply chain, finance and HR domains.


Rohit has a Master's degree in Computer Application (MCA) from the Institute of Management & Technology, Ghaziabad and is a Computer Science graduate from St Stephens College, Delhi University. Rohit is also a Certified Master Black Belt in Six Sigma and a Certified Quality Leader in Six Sigma. He also holds a patent in his name for optimization algorithms designed by him.

Megha Grover | ‎10-10-2013 11:35 AM

The article highlights the role of predictive analytics in today's world. 

Are stochastic models used in predictive analytics to account for the randomness?

SimonatHP | ‎10-23-2013 03:17 AM

Yes, in some of the predictive models stochastic processes are accustomed. For big data, modelers face the issue of overfitting (i.e modeling the randomness instead of event) but using machine learning approaches you can overcome the issue very well

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