Quality Progress - September 2017 - 30

F E AT U R E

BIG DATA

FIGURE 2

Preliminary roadmap of potential standards for big data
Collect

Prepare/
curate

Analyze

Assess data
quality

Scoring full data set and
interface between application
provider and framework
provider

Outliers
identification

Choice of analytical techniques
based on storage platforms

Ensure quality of
input processes
for data quality

Ensure quality of
input processes
for data quality

Validation (TR on survey/
compilation of the existing
scientific literature and
standards on guidance for use

New CRISP

New CRISP

New CRISP

Sampling guidance
+ Random
+ Stratified

Visualize

Access

Ensure quality of
input processes
for data quality
New CRISP

New CRISP

CRISP = cross-industry standard process
TR = technical report

2. To exercise the reference architecture developed by
NIST (Figure 1).
3. To use ISO/TC 69's existing statistical standards
where appropriate for the case study and to identify
gaps in standards within TC 69 to conduct the case
study. These gaps will be important elements to
define a roadmap of future standards for TC 69 to
develop.
The team selected a case study to better understand
the challenges, "get its hands dirty" and achieve these
objectives. The case study chosen was a fraud detection
problem pertaining to Medicare payments to providers.
The 2012 Medicare public data set was used for the
case study and consisted of more than 9 million records
aggregated by unique provider codes (NPI) and procedure (HCCPS) codes.
Further, the data set consisted of 29 columns,
including a unique identifier for the provider, address,
credentials, services aggregated by procedure, unique
beneficiaries of these services, average amount submitted for repayment by Medicare, average allowed by
Medicare and the average paid by Medicare, along with
standard deviations of costs for a given procedure code.9
A specific set of analytic objectives was developed
for the case study, referring to the analytics goals
themselves:
+ Identify suspicious patterns of medical activities
reflected in the sampled data.
+ Document algorithms to be used on the full data set,

30 QP

September 2017 ❘ qualityprogress.com

as well as the updated and appended data sets.
+ Ensure that the tools, techniques and algorithms
apply to other large data sets and, in particular, are
scalable (that is, capable of handling different data set
sizes from different locations) to data sets stored on
multiple nodes.
The team identified three key learning opportunities:
1. Analysts working on the application provider layer
usually do not have the capability to analyze the totality of the data. This is due, in part, to the large size of
the data, which may be stored in multiple nodes and
potentially owned by different parties that use different security systems. This characteristic of big data
requires new methods that deal with the application
of learning from sample data to the original data set.
This, in turn, requires standardization so all parties
know how to interact.
2. Programming code (that is, code used to prepare
and curate the full data set) must be presented to
the information systems practitioner in a language
(JavaScript, C, Python or some other programming
language) that can be executed. This programming
code is passed between the application provider layer
and the data provider (see Figure 1).
3. Because different experts have different backgrounds, time and a willingness to understand each
expert's point of view is required. Consequently, the
dynamics of the team and the importance of the team
leader cannot be minimized.


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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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