Quality Progress - September 2017 - 32

F E AT U R E

BIG DATA

disciplines are necessary to effectively analyze a problem involving big data.
Teamwork-Due to the complexity of data mining
software, analytical software, statistical techniques,
hardware platforms, data structures and the
knowledge required to understand the underlying
processes that generate data, it is imperative that organizations implement a team-based approach.
The Venn diagram shown in Figure 3 illustrates the
need for interactions, communications and teamwork.
Of course, the degree of interactions and communications among the team members depends on the nature
of the problem and the level of skills each member
possesses.
Note that Figure 3 has introduced a team member
called the end consumer. This team member is generally the individual who issued a request for data analysis
or posed a question or problem to be addressed. This
individual receives the output of the big data analysis.
In some cases, the five roles introduced in Figure 3
may be reduced. For example, the quality practitioner
may be the end consumer and the process SME and the
statistician-all depending on the skill level achieved
in each discipline. Regardless of the number of roles
involved, continuous and effective communication is
required, and teamwork is paramount.
Data integrity and quality-This issue is the scorn of
big data. Any meaningful analysis demands a high level
of the integrity and quality of the data. Often, this is not
the case in the big data era. Such was the situation with
the Medicare fraud case study data.
Due to multiple points of data entry (that is, medical
providers across the country and beyond), the data for
a single data field was expressed in different ways. For
example, the "MD" suffix for a medical provider may
have been entered as "MD" or "M.D." Generally, any
analytical software recognizes these data as distinct
data elements. Such situations-and there are many of
them-must be identified separately and corrected. This
requires significant time and effort, as well as teamwork
among the IT practitioner and other team members.
Given these challenges, consider how difficult it would
be for a global organization to analyze quality data
across the organization.
For example, suppose multiple product lines
exist within a site, multiple sites comprise a division, multiple divisions comprise a business unit and
multiple business units comprise the total global
organization. Such a structure is not uncommon. If the

32 QP

September 2017 ❘ qualityprogress.com

FIGURE 3

Teamwork facilitates
successful big data
applications
Process SME

IT practitioner

End
consumer

Statistician
Quality practitioner
SME = subject matter expert

global organization grew through mergers and acquisitions, it would be reasonable to conclude that the
collective quality data across the organization would
constitute a problem of enormous inconsistency. Now
expand the scenario to include multiple global organizations (think industry), and the analysis grinds to a halt.
In such situations, successful results can only be
obtained when data are defined and interpreted in a
uniform manner. For example, the United States has
developed a unified set of state abbreviations (for example, PA, FL, AZ and IL). This set of abbreviations is now
universally accepted and embedded in all data collection
systems-often through the use of drop-down menus.
Similarly, other examples can be readily found.
Incorrect data will never be eliminated, but it can
be minimized. This is a substantial challenge in the big
data era.

More contributions necessary

At its annual meeting in June in Cape Town, South
Africa, TC 69 launched three new work items on big data
analytics on:
+ Statistical terminology.
+ Methodology to flesh out the application provider in
Figure 1 and enhance the development of CRISP-DM
+ Model validation in which a different approach to


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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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