Navigating Big Data Analytics

William D. Mawby

Description

More organizations and their leaders are looking to big data to transform processes and elevate the quality of products and services. Yet, gathering and storing large amounts of data isn't the quick fix often sought after. Without analysts-the human component-to interpret that data, the cost of incorrect or misinterpreted data can greatly impact organizations.
In this book, William Mawby examines the claims of big data analysis in detail. Using examples to illustrate potential problems that may lead to inefficient and inaccurate results, Mawby helps practitioners avoid potential pitfalls and offers application methods to incorporate big data analytics into your company that will enhance your analytic efforts.
William D. Mawby, Ph.D. has extensive consulting, teaching, and project experience and has taught more than 200 courses on many subjects in statistics and mathematics. He is currently writing, teaching courses on climate change and big data, and volunteering at the American Association for the Advancement of Science and the Union of Concerned Scientists.

Sujets

• Data Anlytics
• Data Science
• Computers
• Informatique

Informations

• Publié par: ASQ Quality Press
• Date de parution: 01 juillet 2021
• Nombre de lectures: 1
• EAN13: 9781951058166
• Langue: English

Extrait

Navigating Big Data Analytics
Strategies for the Quality Systems Analyst
Dr. William Mawby

Quality Press Milwaukee, Wisconsin


Published by ASQ Quality Press , Milwaukee, WI
© 2021 by William D. Mawby
Publisher’s Cataloging-in-Publication data
Names: Mawby, William D., 1952-, author.Title: Navigating big data analytics : strategies for the quality systems analyst / by Dr. William D. Mawby.Description: Includes bibliographical resources. | Milwaukee, WI: Quality Press, 2021.Identifiers: LCCN: 2021939486 | ISBN: 978-1-951058-15-9 (paperback) | 978-1-951058-16-6 (epub) | 978-1-951058-17-3 (pdf)Subjects: LCSH Big data. | Data mining. | Quality control. | Statistics—Evaluation. | Statistics—Methodology. | BISAC COMPUTERS / Data Science / Data Analytics Classification: LCC QA276 .M39 2021 | DDC 519.5—dc23
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Table of Contents
Cover
H1585_Mawby_TitlePg
H1585_Mawby_Copyright
H1585_Mawby_Intro
1_H1585_Mawby_Chapter1
2_H1585_Mawby_Chapter2
3_H1585_Mawby_Chapter3
4_H1585_Mawby_Chapter4
5_H1585_Mawby_Chapter5
6_H1585_Mawby_Chapter6
7_H1585_Mawby_Chapter7
8_H1585_Mawby_Chapter8
9_H1585_Mawby_EndNotes
10_H1585_Mawby_Glossary
H1585_Mawby_AA


Introduction
T he message people are hearing today is that all data are good and the more we have of this magic stuff, the better our analyses will be. Indeed, if we follow this chain of reasoning to its logical end, then having enormously large data sets must be the answer to all our problems. At least that’s what we hear from the promoters of big data analytics. The message seems to have achieved a strong level of market penetration judging by frenzied activity in the arena. 1 Who can blame the customer for accepting these claims at face value, since they seem to offer an ideal situation for companies? Who would not like to be able to glean useful information simply by looking at large data sets in clever ways? The fact that the collection of data is becoming easier and cheaper every day would seem to make this “clever” approach a no-brainer. But there might be reason to apply some judicious caution before accepting this information as truth. In this book, we will take a closer look at some of the promises of big data and how some common characteristics of the data themselves can pose challenges for an easy approach.



1
An Introduction to Big Data Analytics
B ig data analytics is defined as the use of algorithms on large data sets to drive decisions that are of value to a company or organization. 2 Often the power of a big data analytics approach is emphasized by describing it as having three “V” words: volume, velocity, and variety .
• Volume refers to the sheer number of data points that are captured and stored. The size of the data sets that are collected can run into terabytes of information—or even larger in some cases.
• Velocity implies that the data are collected more frequently than they have been in the past.
• Variety implies that more kinds of data can be collected and used, including textual and graphical information.
We only need to look at videos that are uploaded to social media to understand the allure of using non-numeric data. The potential of using this kind of data has a rich appeal. Once these vast repositories of data are built, then the promise is that we can mine them, automatically, to detect patterns that can drive decisions to lend value to a company’s activities. The applications of big data analytics run the gamut from customer management through product development through supply chain management.
Consider, for example, the kinds of applications to which big data approaches can be applied to advantage. 3
•The Bank of England is reported to have instituted a big data approach toward the integration of various macroeconomics and microeconomics data sets to which it has access.
•General Electric has invested a lot of effort into creating systems that are efficient at analyzing sensory data so they can integrate production control.
•Xiaomi, a Chinese telephone company, has reportedly used big data to determine the right marketing strategies for its business.
Indeed, organizations that have access to substantial data are trying, in some fashion, to leverage this information to their advantage through big data approaches.
It is also possible to gain an understanding of the scope and size of these big data and data sets by looking at some examples online. Readers can access some typical public data sets that have proved to be useful in this arena. 4 Of course, most business data sets are proprietary and confidential and only accessible to those who are employed by the same companies. In this book, we will depend primarily on artificially constructed data sets in order to focus on the essentials of the problem with big data analytics to prevent us from becoming mired in the details that might be associated with other applications.
For example, the Modified National Institute of Standards and Tech­nology (MNIST) database contains more than 60,000 examples of hand­written digits that can be used in an analysis. Internet Movie Database (IMDb) reviews can provide around 50,000 text-based movie reviews. These examples clearly show how the variety and volume of these different big data sets can be dramatic. The same features that provide big data analysis with some of its most unique applications can also make it impossible to show all the issues that are involved with such efforts.
Many purveyors of big data analysis go even further in their claims by arguing that traditional statistical analyses are likely to be inadequate when applied to very large data sets. They argue that those inadequacies necessitate the development of new data analysis approaches. 5 Most of these new analytic approaches are computationally intensive and extremely flexible in the ways you can use them to interrogate the data. The appli­ca­tion of these new methodologies to uniquely large data sets often is accomplished through the activities of a data scientist whose skill set seems to be a combination of statistics and computer science. Job growth in the area of data science has increased in the last few decades, becoming one of the most highly sought-after positions. All this evidence seems to support the conclusion that big data is becoming essential to the operations of any modern company. It is easy to believe that solutions will appear, as if by magic, once the genie of big data is unleashed.
Deep Learning
At the leading edge of this push to leverage big data is the development of the new field of deep learning. 6 Deep learning is a direct attempt to replace human cognition with a computer 7 that usually relies on using a multilayered neural network to mimic the human brain’s complex structure of synaptic connections. Although deep learning seems to be making some progress, it is nowhere near its ultimate objective to achieve strong artificial intelligence that will replace humans. The dream of artificial intelligence seems to be a world in which the human analytics practitioners have nothing to do but slowly sip their lattes while the algorithm solves all of their problems.
This book aims to address the legitimacy of the claim that big data supporters make: large data sets will be sufficient to accomplish a company’s objectives. We will take a deep dive into the issues that are involved with these approaches and attempt to delineate some apparent boundaries of the big data approach. By providing detailed examples of challenges that can occur commonly in real applications of data analysis, we will belie the conclusion that simply having large data sets will ever be sufficient to replace the human analyst.
When to Use This Technology
Interest in big data has certainly not gone unnoticed by the analysts who are employed in business and industry for the twin purposes of quality and productivity. There is little doubt that most companies are trying hard to find ways to milk this promising new source of information. Anything that can be used to help in solving process problems and improving performance is always of vital interest to these sorts of professionals. Many times, however, it is not clear how to use these new techniques to gain the most value. While not an idle concern, since the speed of modern industry continues to challenge most departments, it is no wonder that many quality practitioners are tempted to think big data analysis is the answer to their prayers. It seems too good to be true that you could get so much out of so little effort. But is this a justified belief? Perhaps things are being over-marketed to some extent, and the best course is to practice caution in adopting these new approaches.
It should be made clear from the outset that this book is not trying to dispute that the use of digital computers has transformed our world in all sorts of ways. This assertion is supported by the many valuable computer algorithms that are being employed today for the purposes of selling tickets, managing