Quality Progress - September 2017 - 22

F E AT U R E

BIG DATA
spreadsheets and pivot-type tables
Data can take several forms, such as
less effective when a large amount of
data at rest and data in motion. Data that
Accomplishing this process of
data is available. They also are much
are sitting in data warehouses waiting
creating an analysis, deploying
more versatile and provide userto be analyzed are referred to as data at
the information and putting it
friendly data aggregation and data
rest (data analyzed after an event occurs)
to use is complex, but disrupviewing. They provide a historical
and are used to create predictive models.
tive analytics play a big role in
view for analytical consideration, but
Data in motion (data analyzed in real
understanding this process.
little or no view of what might haptime as the event occurs, such as click
pen in the future. These software packages have a limited
streams and sensors) flow through a connected device to a
ability to perform predictive analytics, unlike machine
database on the receiving end for immediate analysis. Data
learning. They provide a comprehensive view of isolated
in motion are compared to the predictive models and used to
analytics. Localized views of many variables are possible,
fine-tune the models, and analytics at the edge (performing
but a holistic, end-to-end view of a process is difficult.
analytics at the point where data is collected) deploys inforWhen selecting the best analytical software, it must be
mation to control processes in real time.
flexible enough to handle large amounts of data from data
An example of this create, use and deploy process is elecwarehouses and fast enough to manage streaming data.
tronic fuel injection. Data at rest are used to create a model
There are three analytical tools, or technologies, that
that optimizes fuel distribution, data in motion gathered from
are considered disruptive to historical views of analyzing
test engines are used to fine-tune the model, and the model
processes and data. The perspective of using these tools
is deployed as a controller chip in the vehicle for analytics at
may be new to many quality and Six Sigma professionals.
the edge.
The three disruptive technologies are:
Accomplishing this process of creating an analysis,
1. Machine learning, used to supplement design of experideploying the information and putting it to use is complex,
ments (DoE).
but disruptive analytics play a big role in understanding this
2. Text analysis, used to assist in voice of the customer
process.
(VOC).
3. Discrete event simulation (DES), used to assist in the
Analyzing all that data
analysis of value stream maps (VSM) and theory of
The Toyota Production System1 inspired many people and
constraints.
organizations to pair process improvement with data analysis
These are just three examples. Other disruptive technolto improve how they do business. Historically, the data anaogies exist, such as cluster analysis for reducing variables
lyzed came from low-velocity, low-volume and low-variety
and understanding stratification, and continuous stream
(few variables) processes.
processing to monitor high-velocity data.
In Six Sigma, for example, tools in the define, measure,
analyze, improve and control framework guided teams
through projects in a disciplined fashion. Statistical methods
Machine learning
depended on sampling because the lack of advanced techThe primary objective of machine learning is to automatnology precluded handling high volumes of data easily.
ically iterate through a series of analytical algorithms to
Technology has changed that and has disrupted many samfind the best predictive model. Data from a process are
pling techniques. Now, organizations gather data from sensors
gathered and put into a machine learning model so it can
added to high-speed processes, create data warehouses
learn about the process. The machine learning model
where volumes of that data are available for analysis and
applies what it has learned about the process when anaharvest the information. Business intelligence (BI) software
lyzing new data. It is a way for computers to learn without
packages make it easier to drill through data and are useful to
being programmed.
better understand the current performance of a process.
Historically, modeling has looked for the best fit. But
BI software packages are disruptive because they make
over the years, it became clear that the best fit often overfit the data and didn't provide the best predictive results.
As such, data analysts decided to use holdout data samVisit videos.asq.org/home to see Jim Duarte on ASQTV's
ples to verify the models that were created. By creating a
episode, "Smart Manufacturing and the Future of Technology
model from a substantial portion of the data and evaluatin Manufacturing," and his extended interviews "Taking
ing its accuracy by running it with the holdout data, it was
Control of the Internet of Things" and "Big Data and the
Quality Profession."
easier to iterate to the champion model. This opened a new
horizon when combined with technology to iterate quickly

22 QP

September 2017 ❘ qualityprogress.com


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Table of Contents for the Digital Edition of Quality Progress - September 2017

Seen and Heard
Progress Report
Mr. Pareto Head
Career Coach
Expert Answers
Field Notes
Data Disruption
The Deal With Big Data
Better Intelligence
A Study in Measurement
Innovation Imperative
Statistics Spotlight
Standard Issues
ASQ's 2017 Quality Resource Guide
Marketplace
Footnotes
Back to Basics
Quality Progress - September 2017 - Intro
Quality Progress - September 2017 - cover1
Quality Progress - September 2017 - cover2
Quality Progress - September 2017 - 1
Quality Progress - September 2017 - 2
Quality Progress - September 2017 - 3
Quality Progress - September 2017 - 4
Quality Progress - September 2017 - 5
Quality Progress - September 2017 - Seen and Heard
Quality Progress - September 2017 - 7
Quality Progress - September 2017 - Progress Report
Quality Progress - September 2017 - 9
Quality Progress - September 2017 - Mr. Pareto Head
Quality Progress - September 2017 - 11
Quality Progress - September 2017 - Career Coach
Quality Progress - September 2017 - 13
Quality Progress - September 2017 - 14
Quality Progress - September 2017 - Expert Answers
Quality Progress - September 2017 - Field Notes
Quality Progress - September 2017 - 17
Quality Progress - September 2017 - 18
Quality Progress - September 2017 - 19
Quality Progress - September 2017 - Data Disruption
Quality Progress - September 2017 - 21
Quality Progress - September 2017 - 22
Quality Progress - September 2017 - 23
Quality Progress - September 2017 - 24
Quality Progress - September 2017 - 25
Quality Progress - September 2017 - The Deal With Big Data
Quality Progress - September 2017 - 27
Quality Progress - September 2017 - 28
Quality Progress - September 2017 - 29
Quality Progress - September 2017 - 30
Quality Progress - September 2017 - 31
Quality Progress - September 2017 - 32
Quality Progress - September 2017 - 33
Quality Progress - September 2017 - Better Intelligence
Quality Progress - September 2017 - 35
Quality Progress - September 2017 - 36
Quality Progress - September 2017 - 37
Quality Progress - September 2017 - 38
Quality Progress - September 2017 - 39
Quality Progress - September 2017 - 40
Quality Progress - September 2017 - 41
Quality Progress - September 2017 - A Study in Measurement
Quality Progress - September 2017 - 43
Quality Progress - September 2017 - 44
Quality Progress - September 2017 - 45
Quality Progress - September 2017 - 46
Quality Progress - September 2017 - 47
Quality Progress - September 2017 - Innovation Imperative
Quality Progress - September 2017 - 49
Quality Progress - September 2017 - 50
Quality Progress - September 2017 - Statistics Spotlight
Quality Progress - September 2017 - 52
Quality Progress - September 2017 - 53
Quality Progress - September 2017 - Standard Issues
Quality Progress - September 2017 - 55
Quality Progress - September 2017 - 56
Quality Progress - September 2017 - 57
Quality Progress - September 2017 - ASQ's 2017 Quality Resource Guide
Quality Progress - September 2017 - 59
Quality Progress - September 2017 - 60
Quality Progress - September 2017 - 61
Quality Progress - September 2017 - 62
Quality Progress - September 2017 - 63
Quality Progress - September 2017 - 64
Quality Progress - September 2017 - 65
Quality Progress - September 2017 - 66
Quality Progress - September 2017 - 67
Quality Progress - September 2017 - Marketplace
Quality Progress - September 2017 - 69
Quality Progress - September 2017 - Footnotes
Quality Progress - September 2017 - 71
Quality Progress - September 2017 - Back to Basics
Quality Progress - September 2017 - cover3
Quality Progress - September 2017 - cover4
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