Quality Progress - September 2017 - 31

TC 69 and big data

TC 69 decided to create an ad hoc group to explore the
feasibility of developing standard methods in the area of
big data analytics and in the context of the partnership
with ISO JTC 1 / WG 9 and NIST for the reference architecture they are developing.
The first item on the charter of that ad hoc group was
to conduct a gap analysis to assess the applicability
of 102 existing TC 69 standards and 34 documents in
various stages of development.10
Gap analysis: This task consisted of developing a set
of requirements characterizing the treatment of big data
to evaluate the appropriateness of the TC 69 documents
in the big data world. All 136 documents within TC 69
were evaluated against these requirements.
It was recognized that all 59 control charts, process
management and acceptance sampling documents can
be useful in big data analytics, especially in the preparation and curation phase, as illustrated in Figure 1. This
phase relates to controlling the processes generating
the data or assessing the quality of the data.
TC 69 has 63 of the 136 (46%) documents that can be
viewed as relevant. None of these documents, however,
were initially developed specifically to address the big
data environment. Therefore, they are likely to require
some level of interpretation and modification to accommodate big data.
Preliminary roadmap development: The next item
for the ad hoc group consisted of creating a preliminary
roadmap of documents that TC 69 should consider to
standardize big data analytics. The roadmap shown in
Figure 2 was developed following a discussion on the
impact of the potential changes to existing standards
that will be needed to address big data or the development of brand new documents.
This roadmap is shown to define a portfolio of documents to be developed in such a way that they link
to each other and create a consistent set of tools and
procedures. The roadmap is expected to be revised as
standards are developed.

Challenges in big data analytics

The challenges with regard to big data analytics are
formidable and, hopefully, not insurmountable. A few
key examples include:
Problem formulation-Successful problems are
solved, first and foremost, by defining the problem correctly and succinctly. This is particularly true with regard
to big data analytical problems. With big data sets, the

number of data fields can be enormous, the data fields
may not be fully understood, necessary data fields may
be missing or a direct means of identifying the solution
is not available.
The Medicare fraud case study mentioned earlier
illustrates the difficulty that can be incurred when
trying to formulate a meaningful
problem statement-particularly
with regard to identifying the
The challenges with
solution. Initially, the problem or
regard to big data
question to address was, "Where
analytics are formidoes Medicare fraud exist?" After
dable and, hopefully,
significant and time-consuming
not insurmountable.
analyses, it became clear that a
direct answer to this question did
not exist because the analysis not only pointed toward
real outliers, but potential fraudulent activities.
The reason for this dilemma WAS the simple fact that
no data exist that can readily confirm the presence of
fraud. Statistical tools can only suggest the presence
of fraud. Therefore, the problem statement must be
reformulated as follows: "Where does the potential for
Medicare fraud exist?"
After such situations are identified statistically, further
investigation is reduced to the hands-on approach. Such
an approach might include visits to medical provider
offices, patient interviews and record reviews.
Skill sets-In the past, a quality practitioner would
often analyze processes using a spreadsheet or specialized software such as Minitab, JMP and SAS. Problems
were usually limited to analyzing a single process using
small structured data sets located in a single database
on a single desktop computer.
Frequently, the quality practitioner would produce
a variety of charts (such as control charts and Pareto
charts) and occasionally run a simple linear regression or
two. Problems dealing with determining the underlying
relationships that might exist among multiple variables
(that is, data fields) were often avoided. This occurred
because the quality practitioner did not possess the
appropriate statistical skill level, or the analytical tools
were not available or did not exist.
Enter big data sets, and the landscape changes
significantly as described earlier. You can easily see from
Figure 1 that such a framework necessitates the involvement of information technology specialists, process
subject matter experts (SME), statisticians, quality
practitioners and possibly many others. These skill sets
are not mutually exclusive; However, the individual

qualityprogress.com ❘ September 2017

QP 31


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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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