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Big Data

Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance
Written by: Bernard Marr
Narrated by: Piers Wehner
Length: 6 hrs and 25 mins
4.5 out of 5 stars (3 ratings)

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Publisher's Summary

Get smart - learn to convert the promise of Big Data into real-world results.

There is so much buzz around big data. We all need to know what it is and how it works. But what will set you apart from the rest is actually knowing how to use big data to get solid, real-world business results - and putting that in place to improve performance.

Big Data shows you how to implement the same practices that leading firms have used to access new dimensions of profitability. You'll learn from clear explanations and countless examples how successful organisations large and small use the SMART model to get ahead.

©2015 Bernard Marr (P)2015 Audible, Ltd

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  • Claudio B. Kerber
  • 2016-11-10

It would deserve an A (if it was a school paper)

This book would be a good introduction to a real book about Big Data. There's some good concepts in it, but it barely touches the issue making it sound like a introduction book about data itself, not big data. Dealing with big data brings some issues specific to the task that were not addressed.

Some questions I still have: what are the technologies I can apply in order to deal with huge amounts of data? Where should I store it? What do I plug-in in order to extract some information from it? Where do I get more information? What kind of professionals should I hire? I already have the damned smart questions, I have the datasets, how do I move on? Hiring the author is the only solution?

If my customers ask me about big data I'll have nothing pragmatic to tell them.

10 people found this helpful

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  • Anonymous User
  • 2017-02-14

Meh

Mostly stuff you already know. Really seems like the book was written for an older and less tech savvy person.

4 people found this helpful

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  • GH
  • 2015-09-11

Fluff Data

This book is targeted towards an absolute neophyte who is interested in understanding what the term big data means when he/she heard it advertised by IBM during last year's Superbowl.

What this book does. It takes all the buzz words that can be found by google or Wikipedia, and defines them. Marr attempts to put big data a structure that is too simplistic it's akin to summing up raising a child as: birth, growth, teenage conflict, adulthood. True, but is it worth the credit? NO. This is a pass of "big data" proportions.

22 people found this helpful

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  • Mark Brown
  • 2017-03-16

Light on both Big Data and business acumen

What did you like best about Big Data? What did you like least?

The book is strongest for those with little big data experience making it great for small and medium firms looking to start using the data that they have.

1 person found this helpful

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  • JOSEPH
  • 2017-02-10

Good framework to approach Big Data

Good intro to Big Data. Provides a framework through which one should approach big data within their organization and a good many examples of case studies.

This is not an advanced technical book about how to apply analytics.

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  • Amazon Customer
  • 2018-08-19

Magnificent book, subject and content

Bernard is a reference in this industry and his insights are super useful and good. The book remains an invaluable tool for anyone digging into smart and big data topics and prohedts. I highly recommend it.

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  • Bisi A.
  • 2018-04-06

Interesting book!

The author’s prescience in light of the recent Facebook data privacy debacle made the book relevant to things that are happening today.

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  • Thomas
  • 2018-02-15

Author lacks foundational understanding of statistics

What could have made this a 4 or 5-star listening experience for you?

Okay overview of the application of big data analytics in the business world. My main complaint is that in several places of the book it becomes clear that the author lacks an understanding of the most basic foundations of statistics, as well as the cognitive psychology of decision-making.

For example, when he discusses the downsides of applying big data analytics, he notes that due to the inherent uncertainty of the world, our point predictions will sometimes be wrong (e.g., not everyone who are algorithms singles out as being a risky borrower will actually default). There are two mistakes in this reasoning: Most importantly, one of the main goals of statistics is to QUANTIFY UNCERTAINTY. Thus, no reasonable model would predict that something is going to happen with 100% certainty (like in his examples). Only someone not schooled in statistical thinking would ignore those estimates of uncertainty and simply focus on the point estimate.
Secondly, we have to take into account how humans would decide in the absence of quantitative models. It turns out that the human brain is very bad at thinking probabilistically, and usually thinks in terms of categories and representative examples of these categories. As a result, it is prone to to stereotypes, neglecting the variation within these categories. Thus, the question is not whether statistical models lead us to neglect uncertainty, but whether they neglect uncertainty LESS than human decision-makers would in the absence of quantitative models. (A much better treatment of the risks of big data analytics is found in "Machine, Platform, Crowd", which I can highly recommend.)

Overall, these flaws in the author's thinking makes me question his competence on the subject. Machine learning methods have made statistical models much more powerful, so it is more dangerous than ever if these models get applied without a full understanding of them.



Would you ever listen to anything by Bernard Marr again?

No

What about Piers Wehner’s performance did you like?

It was good

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  • Anonymous User
  • 2017-11-14

similar to other Marr books

already read 2 other Bernard Marr books, all very similar information just told in different ways

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  • Erick Candanedo
  • 2017-11-03

Understand

Geeting to know big data is a matter of urgency. This book must be internalize to make information an every day business.