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Data Driven, by DJ Patil, Hilary Mason
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Succeeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.
You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.
You’ll explore:
- Data scientist skills—and why every company needs a Spock
- How the benefits of giving company-wide access to data outweigh the costs
- Why data-driven organizations use the scientific method to explore and solve data problems
- Key questions to help you develop a research-specific process for tackling important issues
- What to consider when assembling your data team
- Developing processes to keep your data team (and company) engaged
- Choosing technologies that are powerful, support teamwork, and easy to use and learn
- Published on: 2015-01-05
- Released on: 2015-01-29
- Format: Kindle eBook
Most helpful customer reviews
11 of 11 people found the following review helpful.
Data Driven goes nowhere
By Mark Jones Jr.
I expected great things from a book subtitled "Creating a Data Culture." I hoped to read the vision of two data science leaders and glean insight about team and organizational strategies. What I found instead was an uninspiring account of daily tactics used by data technicians and old examples of the decisions driven by data.
Furthermore, in these examples, they do nothing to support their claim that data had anything to do with the success to which they link it. Grammatically speaking, their paragraph has "no data" to support their broad generalizations. Additionally, in at least one example (GE Aviation), their claim is either false or so vague or poorly communicated as to give it the appearance that it is false: it's their suggestion about the use of GE's jet engine data that fails to meet muster. I can say this as an experimental test pilot, mathematician, engineer, and published industry expert.
Finally, their recommendations for "data processes" fall flat. Their recommendations sound more like tactics, something to add to a daily to-do list than processes. They share their own experience rather than broad, far-reaching advice.
1 of 1 people found the following review helpful.
Good baseline look at Data in Business Intelligence
By Andrew Brown
This is an easily consumed paper. It has a solid perspective on how to approach data in the current age. Terms like "big data" get thrown around, but few people can agree what it means. This paper gives some hooks to hang better ideas about data and what it can mean to your organization.
0 of 0 people found the following review helpful.
Good at basics
By Scott
It was basic and easy to read. It did not bridge some gaps on how non-technical people will actually use data. He gives examples where data works but does not where it does not. He implies data is the answer when it has been around forever and is actually just a tool.
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