Think Bigger Developing A Successful Big Data Strategy For Your Business
Mark van Rijmenam
Amazon stars: 4.7
April 2014
Big data–the enormous amount of data that is created as virtually every movement, transaction, and choice we make becomes digitized–is revolutionizing business. Offering real-world insight and explanations, this book provides a roadmap for organizations looking to develop a profitable big data strategy…and reveals why it’s not something they can leave to the I.T. department.
Sharing best practices from companies that have implemented a big data strategy including Walmart, InterContinental Hotel Group, Walt Disney, and Shell, Think Bigger covers the most important big data trends affecting organizations, as well as key technologies like Hadoop and MapReduce, and several crucial types of analyses. In addition, the book offers guidance on how to ensure security, and respect the privacy rights of consumers. It also examines in detail how big data is impacting specific industries–and where opportunities can be found.
Big data is changing the way businesses–and even governments–are operated and managed. Think Bigger is an essential resource for anyone who wants to ensure that their company isn’t left in the dust.
‘The author has clearly taken his time to educate himself of not only the aspects of big data, but also the everyday uses and benefits of using big data. Great read for those looking to improve their knowledge base on the subject.’ Amazon Reviewer Fredrick L. Tipton
Bill Franks
Amazon stars: 5.0
September 2014
The topics of big data and analytics continue to be among the most discussed and pursued in the business world today. While a decade ago many people still questioned whether or not data and analytics would help improve their businesses, today virtually no one questions the value that analytics brings to the table. The Analytics Revolution focuses on how this evolution has come to pass and explores the next wave of evolution that is underway. Making analytics operational involves automating and embedding analytics directly into business processes and allowing the analytics to prescribe and make decisions. It is already occurring all around us whether we know it or not.
‘Analytics without implementation are worthless. The book is simple to read and understand and is a valuable resource if you are technical or in business. If you want to leverage data for positive change within your organization then this is definitely one of the books you should keep on your desk at work.’ Amazon Reviewer J. Thuma
Too Big to Ignore: The Business Case for Big Data
Phil Simon
Amazon stars: 4.7
March 2013
Too Big to Ignore: The Business Case for Big Data is focused on CEO’s CIO’s, presidents, and IT professionals. The book makes a persuasive business case for Big Data. Simon offers common sense advice for organizations looking to make sense out of the information streaming at us with unprecedented volume, velocity, and variety.
‘The book “too Big to Ignore” by Phil Simon is an excellent book for anyone who wants to get a good understanding of the subject of Big Data. He covers this topic in a comprehensive manner that is easy to understand and relevant.’ Amazon Reviewer Jay Oza
Rick Smolan & Jennifer Erwitt
Amazon stars: 4.2
November 2012
The Human Face of Big Data captures, with great photographs and moving essays, an extraordinary revolution sweeping, almost invisibly, through academia, business, healthcare, government, and everyday life. Big Data already enables healthier lives for our children. To provide our seniors with independence while keeping them safe. To help us conserve precious resources like water and energy. To alert us to tiny changes in our health, weeks or years before we develop a life-threatening illness. To peer into our own individual genetic makeup. To create new forms of life. And soon, as many predict, to re-engineer our own species. And we’ve barely scratched the surface
The images and stories captured in The Human Face of Big Data are the result of an extraordinary artistic, technical, and logistical juggling act aimed at capturing the human face of the Big Data Revolution.
‘This book explores the concept of Big Data from so many interesting perspectives. “Big Data” is a broad term, but Rick Smolan makes it accessible by bringing it down to a personal level.’ Amazon Reviewer Dan
Big Data Governance: An Emerging Imperative
Sunil Soares
Amazon stars: 5.0
January 2013
With the arrival of new techniques, organisations are expanding and handling massive volumes of data; this nontechnical book is focused on business audiences. It encourages the practice of establishing appropriate governance over Big Data initiatives and addresses how to manage and govern big data, highlighting the relevant processes, procedures, and policies.
Written by a leading expert in the field, this book focuses on the convergence of two major trends in information management’big data and information governance’by taking a strategic approach oriented around business cases and industry imperatives.
It helps readers understand how big data fits within an overall information governance program; quantify the business value of big data; apply information governance concepts such as stewardship, metadata, and organization structures to big data; appreciate the wide-ranging business benefits for various industries and job functions; sell the value of big data governance to businesses; and establish step-by-step processes to implement big data governance.
‘Sunil Soares’ latest book represents a Rosetta Stone for IT professionals to map their portfolio of traditional IT experiences (master data management, data governance, et al) to the newest data technology trends of “big data” and “big data analytics”. Specifically, among other benefits, it provides a much appreciated reference architecture for big data as well as case studies illustrating the business outcomes from big data analytics.’ Amazon Reviewer Aaron Zornes
Big Data: A Revolution That Will Transform How We Live, Work, and Think
Viktor Mayer-Sch nberger and Kenneth Cukier
Amazon stars: 4.2
March 2013
This lively and well-structured book outlines the development Big Data Analytics has already made in the past years, where we are heading and the dangers associated with the algorithmitisation of the world. This book is a great pick-me-up as an overview of the industry and what is happening. From medical use to business revenue, both international and abroad, these authors understand that the data awakening is upon us, and that that comes with both pluses and negatives.
This book answers questions like Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak? The two leading experts explain in great detail what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data was the first big book about the next big thing.
‘Excellent book that provides a good overview of this very relevant topic. After reading and gaining a foundation on the topic, one will be able to research other areas in greater depth. The book is clear and to the point – adds value and no waste; just what busy professionals need in a good book.’ Amazon Reviewer Jeannette Williams
Numbersense: How to Use Big Data to Your Advantage
Kaiser Fung
Amazon stars: 4.6
July 2013
In Numbersense, expert statistician Kaiser Fung reveals when you should accept the conclusions of the Big Data “experts”–and when you should say, “Wait . . . what?” He investigates a wide range of topics, providing the answers to essential questions, such as:
- How does the college ranking system really work?
- Can an obesity measure solve America’s biggest healthcare crisis?
- Should you trust current unemployment data issued by the government?
- How do you improve your fantasy sports team?
- Should you worry about businesses that track your data?
Numbersense provides you the insight into how Big Data interpretation works–and how it too often doesn’t work. You won’t come away with the skills of a professional statistician. But you will have a keen understanding of the data traps even the best statisticians can fall into, and you’ll trust the mental alarm that goes off in your head when something just doesn’t seem to add up.
‘Kaiser Fung breaks the bad news — a ton more data is no panacea — but then has got your back, revealing the pitfalls of analysis with stimulating stories from the front lines of business, politics, health care, government, and education. The remedy isn’t an advanced degree, nor is it common sense.’ Amazon Reviewer Eric Siegel
Big Data Analytics: Disruptive Technologies for Changing the Game
Dr. Arvind Sathi
Amazon stars: 3.9
February 2013
Bringing a practitioner’s view to big data analytics, this work observes the drivers behind big data, proposes a set of use cases, identifies sets of solution components, and recommends several approaches for implementation. The book also talks about and thoroughly answers key questions, including What is big data and how is it being used? How can strategic plans for big data analytics be generated? and How does big data change analytics architecture?
The author, who has more over 20 years of experience in information management architecture and delivery, has obtained the material from a large amount of workshops and interviews with business and information technology leaders, providing readers with the latest in evolutionary, revolutionary, and hybrid methodologies of moving forward to the brave new world of big data.
‘Most of the current books on big data have either focused on technology (bulk) or business side. This is the first book I read which explains how the 2 aspects come together and create business value! This is a must read for anyone who is deploying or planning to deploy big data solutions in their organization!’ Amazon reviewer Gdesh
Big Data: Principles and best practices of scalable realtime data systems
Nathan Marz and James Warren
Amazon stars: 4.5
January 2015
Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive’rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.
Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they’re built.
Big Data: Understanding How Data Powers Big Business
Bill Schmarzo
Amazon stars: 5.0
October 2013
Social media analytics, web-analytics, and other techniques enable organisations to acquire and handle massive amounts of data to better understand their customers, competitors, products and markets. Using these insights, organizations can improve their products, their services, add value and increase the ROI. The tricky part for busy IT professionals and executives is how to get this done, and that’s where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to drive business value with big data.
The book is filled with practical techniques, real-world use cases, and hands-on exercises. It explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Big Data: Understanding How Data Powers Big Business is written by one of Big Data’s preeminent experts, William Schmarzo. Don’t miss his invaluable insights and advice.
‘Bill’s book is an excellent, practical approach to big data. The first half of the book is about big data and how it can help businesses make more money. The second half of the book is Bill’s methodology, step-by-step approach, to begin a corporate strategy around big data.’ Amazon Reviewer David Sarson
The Signal and the Noise: Why Most Predictions Fail- But Some Don’t
Nate Silver
Amazon stars: 4.3
September 2012
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger’all by the time he was thirty. He solidified his standing as the nation’s foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.
Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the ‘prediction paradox’: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.
‘This is the best general-readership book on applied statistics that I’ve read. Short review: if you’re interested in science, economics, or prediction: read it. It’s full of interesting cases, builds intuition, and is a readable example of Bayesian thinking.’ Amazon Reviewer Sitting in Seattle
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Eric Siegel
Amazon stars: 4.2
February 2013
This book is easily understood by all readers. Rather than a “how to” for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques. You have been predicted by companies, governments, law enforcement, hospitals, and universities. Their computers say, “I knew you were going to do that!” These institutions are seizing upon the power to predict whether you’re going to click, buy, lie, or die.
Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.
How? Prediction is powered by the world’s most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.
Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future lifting a bit of the fog off our hazy view of tomorrow means pay dirt.
‘Interesting book for beginners and people curious about PA. The examples are explained in easy and understandable way without any technical jargon. I recommend this position as a start point for those who want to apply PA. Great introduction for further more technical readings.’ Amazon Reviewer Prz
Fast Data and The New Enterprise Data Architecture
Scott Jarr
Amazon stars: N/A
October 2014
Big Data is transforming the way enterprises interact with information, but that’s only half the story. The real innovations are happening at the intersection of Fast Data and Big Data. Download this eBook today to learn more about Fast Data and the new enterprise data architecture’a unified data pipeline for working with Fast Data (data in motion) and historical Big Data – together.
The book covers among others what the capabilities of a fast/big data pipeline are ‘from high-velocity operations to historical analysis. It has multiple examples that show the benefits of fast data as well as the requirements for building fast data applications.
