|Learning To Love Data Science|
Author: Mike Barlow
This collection of articles discusses big data use from a non-technical viewpoint.
This is a slim book that introduces a variety of ideas about big data and how it is being used. It is aimed at managers and people who've not got to grips with the ideas behind big data, and hence has nothing to offer developers.
The book is actually a collection of articles on various topics related to the way data and its analysis are being used. The author is a journalist and ex-editor at titles including The Journal News and the Stamford Advocate. More recently, he's written reports for O'Reilly around the topic of big data, and this book is a collection of those reports.
The subtitle of the book is “Exploring Predictive Analytics, Machine Learning, Digital Manufacturing, and Supply Chain Optimization”, but that makes it sound more technical than it actually is. Each article/chapter gives an introduction to the ideas, and includes some quotes from people who were product managers or developers at technology companies at the point the article was written. One of the problems for the book is the age of some of the chapters - they range in date from 2013 to 2015, but the earlier ones are already showing their age.
The book opens with a chapter on the culture of big data analytics that attempts to describe what a typical data scientist is like, with discussions from people at Mastercard, Citigroup and SAS.
Next is a chapter on Data and Social Good, where Barlow focuses on a number of philanthropic data projects such as one that analyses applications for subsidised solar energy to ensure the best recipients are chosen.
A chapter asking 'Will Big Data Make IT Infrastructure Sexy Again?' comes next, arguing that while the technology might be seen as 'cool', the spending will happen because the new kit will make it possible to do analyses that make economic sense.
A chapter on smart machines and the Internet of Things argues that these technologies will open possibilities for indie manufacturers and tech startups. One on Real-Time Big Data Analytics is essentially arguing that people mean different things by the term 'real-time'; while a chapter on Big Data and the Evolving Role of the CIO puts forward the idea that big data will finally make CIOs really board level.
There's a chapter on building function teams, and another on predictive maintenance using a combination of sensor-equiped machines, networks, and advanced analytics. One chapter that was more original than others argued that it's difficult to have data security and rapid business innovation, in which the author points out that things have moved more rapidly than many companies expected, resulting in some of the recent headline data leaks. The book ends with a chapter on the problem of making big data more accessible.
Overall, there were a few interesting ideas scattered through this book. It's quite lightweight, and because the original articles are several years old, it labors points that now seem obvious.
|Last Updated ( Tuesday, 26 January 2016 )|