Summary of Mind + Machine

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Rating

8 Overall

8 Applicability

8 Innovation

7 Style


Recommendation

Evaluserve CEO Marc Vollenweider knows his analytics, and large stretches of his guidebook offer focused, applicable advice. The list of analytics-related fallacies in Part I alone can save organizations plenty of money, and the methodology he explains in Part III can make implementing analytics sensible and functional. However, because Vollenweider is so experienced in analytics, he moves quickly, and so his work may challenge those who are new to the field. That issue aside, getAbstract recommends his guidance to anyone interested in analytics, big data or knowledge management.

In this summary, you will learn

  • How most businesspeople misunderstand analytics,
  • What trends offer great possibilities for analytics and
  • How to implement analytics strategically.
 

About the Author

Marc Vollenweider is co-founder and CEO of Evaluserve, a company that specializes in analytics and data management.

 

Summary

Big Data: Hype, Fallacies and Reality

Big data and related fields like analytics suffer from hype. Overblown claims about big data often overwhelm useful, focused, rational discussions of big-data reality. The first fallacy is that “big data solves everything.” It doesn’t. Many people embrace big data without necessary tools and contexts. They need better structures to govern data usage, as well as a definition of how they’ll use analytics, goals, metrics and accountable, expert staff.

Some people falsely believe you need a “data lake and tools.” A data lake is a huge collection of data. The fallacious promise is that you can process data more cheaply if you collect mass quantities of it. Unfortunately, you’re more likely to duplicate data, let data die in the lake and risk handling “intellectual property” badly. People also believe that more data “means more insight.” That can be true – if you process the right data the right way. More often, it isn’t true.

A Data Pyramid

Consider a pyramid of data use with four levels. Level 1 is “raw data,” like surveillance camera photos. Level 2 is “information,” data you...


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    Timothy Chapman 1 year ago
    Touches on good key topics and is good reading on tools and rick management.
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    Pedro A Correa 2 years ago
    Touches on key topics such as human rationality, tools and risk management without deep diving and losing focus. Could've structured more clearly: trends, why outsourcing becomes favored with this approach. Good reading. From 0 to 10, as far as satisfying my expectations, I give it a 7.5.