Here is How Big Data is changing the Oil Industry
In 2006, marketing commentator Michael Palmer had blogged, “Data is just like crude. It’s valuable, but if unrefined it cannot really be used.” After nine years, th...
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If Data is as Valuable as Gold, It’s Time to Polish Your Data Architecture
It speaks volumes of the world we live in today when headlines such as “The world’s most valuable resource is no longer oil, but data” and “Why Data May Be More V...
The Data Scientist: a New and Necessary Profession
The advancement of new technologies and the development of big data require professionals with skills in many fields: computer science, mathematics, statistics and busine...
Data readiness strategies of AI Start-ups
Last week, at an event on AI, I asked the panel about how investors evaluate the Data readiness of AI start-ups. This subject is close to my work and my teaching. I teach...
DataOps – It’s a Secret
Summary: DataOps is a series of principles and practices that promises to bring together the conflicting goals of the different data tribes in the organization, data sc...
Charting a Mandate for a Chief Data Scientist
The profusion of big data alongside helpful nudges from Wall Street has inspired many companies to create Chief Data Officer(CDO) and Chief Data Scientist(CDS) roles. The...
Data Devils Snapping At Your Heals
Ever wonder how you can create meaningful insights with your data? Tired of being asked what’s your big data strategy, are you doing predictive analytics, or how yo...
Alternatives to algebraic modeling for complex data: topological modeling via Gunnar Carlsson
For many, mathematical modeling is exclusively about algebraic models, based on one form or another of regression or on differential equation modeling in the case of dyna...
Finding "Gems" in Big Data
(Photo credit: Rob Lavinsky, iRocks.com – CC-BY-SA-3.0) In 1945, Count ,Richard Taaffe* a Dublin gem collector, was sorting through a set of spinel gems that he had...
How to think like a Data Scientist
A data scientist needs to be Critical and always on a lookout for something that misses others. So here is some advice that one can include in the day to day data science...