Opening book details…
Can I read Too Big to Ignore : The Business Case for Big Data on EtoBox?
Too Big to Ignore : The Business Case for Big Data by Simon, Phil is a business book available to read on EtoBox.
What is Too Big to Ignore : The Business Case for Big Data about?
__Too Big to Ignore: The Business Case for Big Data__ is geared towards CIOs, CEOs, presidents, and IT professionals. At a high level, the book makes a compelling business case for that which we are calling __Big Data__. Simon provides commonsense advice for organizations looking to make sense out of the information streaming at us with unprecedented volume, velocity, and variety. Think big.
Who reads Too Big to Ignore : The Business Case for Big Data?
It is typically read by working professionals who need an authoritative practice reference.
Common subject areas: medicine, law, business, engineering.
- Author
- Simon, Phil
- Publisher
- John Wiley & Sons, Incorporated
- Published
- 2012
- Language
- EN
- ISBN
- 9780521810999
- Category
- business
- Subjects
- Business, Mathematics, Management
- Rating
- 3.77 / 5 (52 ratings)
- Updated
- 2026-03-14
Other editions & translations
More by Simon, Phil
Browse all works by Simon, Phil
Similar books
- Big Data : Understanding How Data Powers Big Business — Bill Schmarzo (2013)
- Big Data Analytics : Harnessing Data for New Business Models — Soraya Sedkaoui , Mounia Khelfaoui , Nadjat Kadi (2021)
- Big Data For Dummies — Judith S. Hurwitz; Alan Nugent; Fern Halper; Marcia Kaufman (2013)
- Big Data Management: Data Governance Principles for Hadoop and Big Data Analytics — Peter Ghavami; Walter de Gruyter GmbH & Co. KG (2020)
- Big Data, Data Mining, And Machine Learning: Value Creation For Business Leaders And Practitioners Wiley And Sas Business Series Big Data; Data Mining; And Machine Learning Big Data, Data Mining And Machine Learning Wiley & Sas Business Series Big Data, Data Mining, And Machine Learning Wiley And Sas Business — Khosrow Hassibi; Jared Dean (2014)
- Big Data Bootcamp : What Managers Need to Know to Profit From the Big Data Revolution — David Feinleib (2014)