Organizations of all sizes are striving to become data driven, starting in earnest with the rise of big data a decade ago. With the never-ending growth in data sources and methods for aggregating and analyzing them, the use of data to direct the business has become a requirement. Randy Bean has been helping enterprise organizations define and execute their data strategies since before the age of big data. In this episode he discusses his experiences and how he approached the work of distilling them for his book "Fail Fast, Learn Faster". This is an entertaining and enlightening exploration of the business side of data with an industry veteran.
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Your host is Tobias Macey and today I’m interviewing Randy Bean about his recent book focusing on the use of big data and AI for informing data driven business leadership
Interview
Introduction
How did you get involved in the area of data management?
Can you start by discussing the focus of the book and what motivated you to write it?
Who is the intended audience, and how did that inform the tone and content?
Businesses and their officers have been aiming to be "data driven" for years. In your experience, what are the concrete goals that are implied by that term?
What are the barriers that organizations encounter in the pursuit of those goals?
How have the success rates (real and imagined) shifted in recent years as the level of sophistication of the tools and industry for data management has increased?
What is the state of data initiatives in leading corporations today?
What are the biggest opportunities and risks that organizations focus on related to their use of data?
At what level(s) of the organization do lessons around data ethics need to be embedded?
You have been working with large companies for many years to help them with their adoption of "big data". How has your work on this book shifted or clarified your perspectives on the subject?
What are the main lessons or ideas that you hope readers will take away from the book?
What are the most interesting, innovative, or unexpected ways that you have seen big data applied to business?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on this book?
What are your predictions for the next decade of big data and AI?
