Quality Progress - September 2017 - 29

Primary classes of big data problems

The 2016 ASQ Global State of Quality reports included
a spotlight report titled "A Trend? A Fad? Or Is Big Data
the Next Big Thing?"3-6 hinting that big data is here to
stay. If the conversion from acceptance sampling, control charts or design of experiments seems a world away
from the tools associated with big data, rest assured
that the statistical bases still apply.
Of course, the actual data, per the four V's, are
different. Relevant formulations of big data problems,
however, enjoy solutions or approaches that are statistical, though the focus is more on retrospective data
and causal models in traditional statistics, and more
forward-looking data and predictive analytics in big
data analytics.7
Two primary classes of problems occur in big data:
+ Supervised problems occur when there is a dependent variable of interest that relates to a potentially
large number of independent variables. For this,
regression analysis comes into play, for which the typical quality practitioner likely has some background.
+ Unsupervised problems occur when unstructured
data are the order of the day (for example, doctor's
notes, medical diagnostics, police reports or internet
transactions). Unsupervised problems seek to find the
associations among the variables. In these instances,
cluster and association analysis can be used. The quality practitioner can easily pick up such techniques.

The world of standardization
and big data analytics

Many fields of expertise share an interest in big data
analytics, including statistics, artificial intelligence, information systems, and other fields in which the data are
collected in great quantities (marketing, quality, finance,
supply chain and engineering, for example).
The convergence of so many technical areas motivates standardization activities in areas such as
terminology, algorithms and reporting. A search for
big data analytics on the International Organization
for Standardization (ISO) website, in fact, identified 54
items that include standards, reports and guidelines.
Most of these come from Joint Technical Committee
1 (JTC 1) Information Technology. Others come from
vertical committees, such as information security management, energy, transportation and farming.
ISO/IEC JTC 1 has established Working Group 9
(WG 9) to develop foundational standards for big
data, including reference architecture and vocabulary

standards for guiding big data efforts throughout JTC 1
upon which other standards can be developed.
NIST is the key contributor to JTC 1/WG 9. Its goal is
to develop a standard interoperability framework that
offers scientists and other experts an architecture to
ease the process of big data analytics-whether during
the collection, storage, analysis, or deployment of models or any other phase encountered in the big data arena.
The NIST Reference Architecture has been documented
Many fields of expertise
in a seven-volume publication
share an interest in big
that can be downloaded from
data analytics, includthe NIST website and is refing statistics, artificial
erenced as the NIST Big Data
intelligence, informa8
Interoperability Framework.
tion systems, and other
Figure 1 shows a high-level
fields in which the data
view of the architecture.
are collected in great
The application provider
quantities.
in Figure 1 is the area in which
ISO Technical Committee
(TC) 69 Applications of Statistical Methods, statisticians
and quality practitioners, are positioned. There are three
opportunities to interface with other fields of expertise:
1. The data provider provides or feeds data or information into the big data system.
2. The framework provider executes certain statistical
algorithms while protecting the privacy and integrity
of the data.
3. The data (end) consumer in this framework constitutes end users or other systems that use the results
of the big data application provider.
It is interesting to see that visualization-usually a
step in the analysis that follows the data preparation-is
now a step following the actual analysis of the data. The
rationale is that visualization of the entire data set is
unwieldy, if not impossible, while visualization of results
at the post-analysis (that is, the presentation) stage
facilitates understanding.

Joint partnership with
JTC 1/WG 9 and NIST

Given the multidisciplinary nature of big data analytics, it
is a natural step for ISO/TC 69 to establish a liaison with
JTC 1/WG 9. A small joint team of TC 69 experts and JTC
1/WG 9 experts, along with NIST experts, was assembled
with the following business and analytics objectives:
1. To mimic-as much as possible-the operation of a
data mining or business analytics team assembled to
address a case study selected by the team.

qualityprogress.com ❘ September 2017

QP 29


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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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