• Lars Christensen

Big data @ work by Thomas Davenport - 2 minute read


Date read: 2020-08-10. How strongly I recommend it: 7/10

Are you looking for a book about BIG DATA? This book will get you a good starting point.

I’m trying to learn more about how big data and analytics can help decision making in my role at Autodesk. 

Get it on Amazon.

The book is breaking the topic down into useful sections, like:

  • The technology

  • The history

  • How it impacts small organizations

  • How it impacts big organizations 

  • The human side of big data

  • How to succeed with big data

My notes and thoughts:

  • P17. One problem is that management is monitoring data without really making real decisions. Numbers are up…..Now they are down….Now they are up again, Hooray. Without having clear lines and matrix’s that leads to decision making it really leads to nothing.

  • P21. Quote from Peter Drucker, about how big corporations are thinking inward on costs and efforts, rather than outward on opportunities, changes, and threats.

  • P22. There are three classes of value:

  • Cost reduction opportunities 

  • Decision improvements

  • Improve products and services

  • P57. It could be interesting to draw up the “Ten years from now” CS customer journey and set up that as a goal

  • P67. How banks are using big data to funnel website clicks and user records to break down to useable data.

  • P71. The outcome from big data is more about incremental improvements than the grand breakthroughs

  • P77. Approach big data as two-pronged approach

  • One is to figure out what data you have available. Are you sitting on a goal-mine of data that you can do?

  • Two, Now track down the application you can pursue with the data available. Figure out what goals you want to pursue.

  • P114. Overview of technologies for big data

  • Hadoop: Open-source software for processing big data across multiple parallel servers.

  • MapReduce: The architectural framework on which Hadoop is based.

  • Scripting languages: Programming languages that work well with big data(Phyton, Pig, Hive)

  • Machine learning

  • Visual analytics

  • P134. Who does big data at Autodesk?

  • P144. Targets for big data, starting points:

  • Where do we have significant data resources that are unexploited?

  • Which of our business processes are most in need of better decision making?

  • Where would we benefit from much faster decision making?

  • Are we processing large amounts of data that would benefit in the terms of cast reduction from big data technology?

  • How might we create data-based products or services, and in which part of our business would they be most relevant and useful?

  • Is someone else in our industry likely to employ big data in a way that will disadvantage us? If so, How are they likely to use it?

  • P155. Does the CS team use the new analytic tool within Fusion 360 to help guide customers?

  • P188. Make sure to have an executive sponsor when diving into a project, like, Big Data.

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