Gartner Blog Network

Jitendra Subramanyam
VP, Team Manager I
2 years at Gartner
16 years IT Industry

Jitendra Subramanyam leads a research team that is focused on how Chief Data Officers manage the Data and Analytics function in their organizations. Jitendra teaches a course on machine learning at Harvard Extension School. The course is a practical introduction meant to help business executives understand key concepts and techniques in data science and immediately apply them to business problems.Read Full Bio

New November Publications

by Jitendra Subramanyam  |  November 4, 2019

The Chief Data & Analytics Officer Research Team published the following last month: Data & Analytics Talent: Capability-Based Data & Analytics Talent (Stats NZ) https://www.gartner.com/doc/3970761 D&A talent is expensive and hard to find. Learn how Stats NZ attracts the right talent by prioritizing core capabilities rather than technical skills in its recruiting. Find out how […]

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New October Publications

by Jitendra Subramanyam  |  October 7, 2019

The Chief Data & Analytics Officer Research Team published the following last month: Data & Analytics Strategy and Planning: Capability-Drive Data Use Expectations (Bunge) https://www.gartner.com/doc/3969807 Do your business partners bog down your D&A team with endless requests for reports and dashboards? Find out here how Bunge standardized its business partners’ requests to ensure its data […]

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New September Publications

by Jitendra Subramanyam  |  September 6, 2019

The Chief Data and Analytics Officer Research Team published the following last month: Data & Analytics Quality and Ethics: Rationales for the Idealistic Imperative in Business https://www.gartner.com/doc/3957016 Are you perplexed by the hype around business ethics? Click here to learn why ethics are crucial for branding and financial performance today. Find concrete recommendations D&A leaders […]

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Pitfalls of Algorithmic Decisions and How to Handle Them

by Jitendra Subramanyam  |  August 24, 2019

This post is by Veena Variyam, Director, Infrastructure & Operations Advisory and Research at Gartner. Algorithmic Decisions (and Pitfalls) are Everywhere Machine learning algorithms with access to large data sets are making myriads of critical decisions, such as medical diagnoses, welfare eligibility, and job recruitment, traditionally made by humans. However, high-profile incidents of biases and […]

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New August Publications

by Jitendra Subramanyam  |  August 1, 2019

The Chief Data & Analytics Officer Research Team published the following NEW RESEARCH last month: Data & Analytics Strategy and Planning: 5 Steps to Get Started with Machine Learning https://www.gartner.com/doc/3953653 Eager to get started with ML but afraid it will be too technically difficult, expensive, or time consuming? Click here to learn the 5 steps […]

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Data-Driven Decision Making

by Jitendra Subramanyam  |  July 22, 2019

Here is some data. You’ve probably seen something like this in your management dashboard. Does it warrant a data-driven decision? You are in charge of Group 4. Given that your group’s renewal rate is lower than that of Groups 1, 2, and 3, should you take action to fix customer renewal in your group? Should […]

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When Machine Learning Prediction Excels

by Jitendra Subramanyam  |  July 6, 2019

Meet the Chief Data and Analytics Officer research team | Check out our research In the previous post, Prediction Models: Traditional versus Machine Learning, we looked at 3 kinds of prediction models and clarified the difference between traditional and machine learning models for prediction. In this post we’ll see that machine learning prediction models excel […]

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Prediction Models: Traditional versus Machine Learning

by Jitendra Subramanyam  |  June 8, 2019

Meet the Chief Data and Analytics Officer research team | Check out our research Machine learning models are constructed differently from traditional quantitative models. Two Types of Traditional Prediction Models In the first type of traditional prediction model, the input data set along with statistical assumptions and calculations determine the prediction algorithm. The input data […]

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How Long Should You Look Before You Leap?

by Jitendra Subramanyam  |  May 15, 2019

Meet the Chief Data and Analytics Officer research team | Check out our research In his fascinating book, A Man For All Markets, Edward Thorp trots out a problem called the “secretary problem” and breezily gives us the solution (pp.263-4). “Assume that you will interview a series of people, from which you will choose one. […]

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Chief Data and Analytics Officer Research Publication List

by Jitendra Subramanyam  |  May 3, 2019

Here is a list of our publications to date – it is kept current as we release more publications. [Last updated: November 4, 2019] The Team Jitendra Subramanyam  https://www.linkedin.com/in/jsubramanyam/ Ben Hertzberg https://www.linkedin.com/in/benjaminhertzberg/ Farhod Yuldashev https://www.linkedin.com/in/fyuldash/ Ethan Green https://www.linkedin.com/in/ethanfgreen/ Ariel Silbert https://www.linkedin.com/in/ariel-silbert-289a50104/ Shelly Thackston https://www.linkedin.com/in/shellythackston/ Our research terrain covers: Business Value of Data & Analytics Data & Analytics Strategy and Planning Data […]

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