Quality Progress - September 2017 - 36

F E AT U R E

DATA AND IT
These tools integrate historical information with current information to provide descriptive (what happened),
diagnostic (why it happened), predictive (what will happen) and prescriptive (what should be done) analytics.4
+ Descriptive analytics focus on reporting past events
and providing alerts about current events. Techniques
used include statistical modeling and experimental
design.
+ Diagnostic analytics focus on pinpointing why it happened. Techniques used include data discovery, data
mining, drill-down and correlations.
+ Predictive analytics focus on predicting future
events. Techniques used include modeling, pattern
recognition, machine learning, natural language processing and forecasting.
+ Prescriptive analytics focus on prescribing actions
to be taken for future business performance. Techniques used include simulation, optimization and
extrapolation.
Advanced analytics tools combine various mining
techniques, such as:5
+ Data mining, which deals with data stored in relational
databases.
+ Text mining, which deals with data stored in text
format.
+ Web mining, which can be further divided into three
mining types: Web content mining deals with the
FIGURE 1

Advanced analytics
Data sources
selection

+ Internal data sources
+ External data sources

Advanced
analytics

+ Data collection (internal, external,
structured, unstructured and semi-structured)
+ Data fusion
+ Data mining
+ Text mining
+ Web mining (content, structure and use)
+ Speech mining
+ Link mining
+ Image mining
+ Video mining
+ Statistical data analysis
+ User-friendly tools and models
(descriptive, predictive and prescriptive)

Lean Six Sigmabased quality
management system

36 QP

+ Fact-based decision making
and problem solving (strategic,
tactical and operational)
+ Performance excellence
+ Stakeholder satisfaction

September 2017 ❘ qualityprogress.com

content on websites. Web structure mining deals with
the structure of websites on the internet. Web use
mining deals with data stored in web server logs. This
is also known as web log mining.6
+ Link mining, which deals with associations between
and among entities.
+ Image mining, which deals with image data.
+ Video mining, which deals with video data.
+ Speech mining, which deals with audio data.
Figure 1 represents the typical design of advanced
analytics in organizations.

Data sources

Analytics and tools need data, of course. Data sources
may include:
+ Vast amounts of unstructured digital data stored
within organizations in the form of emails, audit
reports, call center notes, call center logs, intranet
logs, intranet comments, survey notes, internal Wikis,
blogs, internal social networking sites, technical documentation, internal knowledgebase, vendor alerts,
test defects, internal research reports and employee
performance reports.
+ Vast amounts of unstructured digital information
stored on social media websites, such as Wikis, blogs,
Facebook, LinkedIn, Google+, YouTube and Twitter.
+ Vast amounts of semi-structured information, such as
XML or EDI exchanged among organizations.
+ Structured historical, operational and transactional
information available in database management
systems.

Advanced analytics

Advanced analytics are a collection of tools
used to gather required data from selected
data sources, perform data fusion, apply mining
algorithms, perform traditional data analysis
and provide user-friendly tools and models.
Data fusion includes the cleanup, preprocessing,
transformation and integration of data.7
Algorithms used for mining include decision
trees, cluster analysis, neural networks, classification analysis, anomaly detection, association
analysis and regression. User-friendly tools
include visualization systems, dashboards and
recommendation systems.

Lean Six Sigma-based QMSs

Integrating advanced analytics with lean Six Sigma-based quality management systems (QMS)


http://www.qualityprogress.com

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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