Data-Ed: Unlock Business Value through Data Quality Engineering

Unlock Business Value through
Data Quality Engineering
Presented by Peter Aiken, Ph.D.
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

Copyright 2013 by Data Blueprint
2
Unlock Business Value through Data Quality Engineering
Organizations must realize what it means to utilize data
quality management in support of business strategy. This
webinar focuses on obtaining business value from data
quality initiatives. I will illustrate how organizations with
chronic business challenges often can trace the root of the
problem to poor data quality. Showing how data quality
should be engineered provides a useful framework in which
to develop an effective approach. This in turn allows
organizations to more quickly identify business problems as
well as data problems caused by structural issues versus
practice-oriented defects and prevent these from re-
occurring.
Date: June 11, 2013
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.
Time:
• timeliness
• currency
• frequency
• time period
Form:
• clarity
• detail
• order
• presentation
• media
Content:
• accuracy
• relevance
• completeness
• conciseness
• scope
• performance
Time:
• timeliness
• currency
• frequency
• time period
Form:
• clarity
• detail
• order
• presentation
• media
Content:
• accuracy
• relevance
• completeness
• conciseness
• scope
• performance

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Meet Your Presenter:
Peter Aiken, Ph.D.
• 25+ years of experience in data management
• Multiple international awards &
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• President, DAMA International (dama.org)
• 8 books and dozens of articles
• Experienced w/ 500+ data management
practices in 20 countries
• Multi-year immersions with organizations as
diverse as the US DoD, Nokia,
Deutsche Bank, Wells Fargo, and the
Commonwealth of Virginia
4

1. Data Management Overview
2. DQE Definitions (w/ example)
3. DQE Cycle & Contextual Complications
4. DQ Causes and Dimensions
5. Quality and the Data Life Cycle
6. DDE Tools
7. Takeaways and Q&A
Outline
5

Data Program
Coordination
Feedback
Data
Development
Standard
Data
Five Integrated DM Practice Areas
Organizational Strategies
Goals
Business
Data
Business Value
Application
Models &
Designs
Implementation
Direction
Guidance
6
Organizational
Data Integration
Data
Stewardship
Data Support
Operations
Data
Asset Use
Integrated
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable
data access
Achieve sharing of data
within a business area

Five Integrated DM Practice Areas
7
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program
Coordination
Organizational
Data Integration
Data
Stewardship
Data
Development
Data Support
Operations

• 5 Data Management
Practices Areas / Data
Management Basics
• Are necessary but
insufficient
prerequisites to
organizational data
leveraging
applications
(that is Self Actualizing
Data or Advanced
Data Practices)
Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/
AAAAAAAABgw/Ib-nVkMmMEQ/s1600/
maslows_hierarchy_of_needs.png
Advanced
Data
Practices
• Cloud
• MDM
• Mining
• Analytics
• Warehousing
• Big
Data Management Practices Hierarchy (after Maslow)

Data Management
Body of
Knowledge
9
Data
Management
Functions

• Published by DAMA International
– The professional association for
Data Managers (40 chapters worldwide)
– DMBoK organized around
• Primary data management functions focused
around data delivery to the organization (dama.org)
• Organized around several environmental elements
• CDMP
– Certified Data Management Professional
– DAMA International and ICCP
– Membership in a distinct group made up of your
fellow professionals
– Recognition for your specialized knowledge in a
choice of 17 specialty areas
– Series of 3 exams
– For more information, please visit:
• http://www.dama.org/i4a/pages/index.cfm?pageid=3399
• http://iccp.org/certification/designations/cdmp
DAMA DM BoK & CDMP
10

Overview: Data Quality Engineering
11

6. DDE Tools
Outline
12

Data
Data
Data
Information
Fact Meaning
Request
A Model Specifying Relationships Among Important Terms
[Built on definition by Dan Appleton 1983]
Intelligence
Use
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one
MEANING.
5. INTELLIGENCE is INFORMATION associated with its USES.
Wisdom & knowledge are
often used synonymously
Data
Data
Data Data
13

Definitions
• Quality Data
– Fit for use meets the requirements of its authors, users,
and administrators (adapted from Martin Eppler)
– Synonymous with information quality, since poor data quality
results in inaccurate information and poor business performance
• Data Quality Management
– Planning, implementation and control activities that apply quality
management techniques to measure, assess, improve, and
ensure data quality
– Entails the "establishment and deployment of roles, responsibilities
concerning the acquisition, maintenance, dissemination, and
disposition of data" http://www2.sas.com/proceedings/sugi29/098-29.pdf
✓ Critical supporting process from change management
✓ Continuous process for defining acceptable levels of data quality to meet business
needs and for ensuring that data quality meets these levels
• Data Quality Engineering
– Recognition that data quality solutions cannot not managed but must be engineered
– Engineering is the application of scientific, economic, social, and practical knowledge in
order to design, build, and maintain solutions to data quality challenges
– Engineering concepts are generally not known and understood within IT or business!
14
Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166

Improving Data Quality during System Migration
15
• Challenge
– Millions of NSN/SKUs
maintained in a catalog
– Key and other data stored in
clear text/comment fields
– Original suggestion was manual
approach to text extraction
– Left the data structuring problem unsolved
• Solution
– Proprietary, improvable text extraction process
– Converted non-tabular data into tabular data
– Saved a minimum of $5 million
– Literally person centuries of work

Unmatched
Items
Ignorable
Items
Items
Matched
Week # (% Total) (% Total) (% Total)
1 31.47% 1.34% N/A
2 21.22% 6.97% N/A
3 20.66% 7.49% N/A
4 32.48% 11.99% 55.53%
… … … …
14 9.02% 22.62% 68.36%
15 9.06% 22.62% 68.33%
16 9.53% 22.62% 67.85%
17 9.50% 22.62% 67.88%
18 7.46% 22.62% 69.92%
Determining Diminishing Returns
16

Time needed to review all NSNs once over the life of the project:Time needed to review all NSNs once over the life of the project:
NSNs 2,000,000
Average time to review & cleanse (in minutes) 5
Total Time (in minutes) 10,000,000
Time available per resource over a one year period of time:Time available per resource over a one year period of time:
Work weeks in a year 48
Work days in a week 5
Work hours in a day 7.5
Work minutes in a day 450
Total Work minutes/year 108,000
Person years required to cleanse each NSN once prior to migration:Person years required to cleanse each NSN once prior to migration:
Minutes needed 10,000,000
Minutes available person/year 108,000
Total Person-Years 92.6
Resource Cost to cleanse NSN's prior to migration:Resource Cost to cleanse NSN's prior to migration:
Avg Salary for SME year (not including overhead) $60,000.00
Projected Years Required to Cleanse/Total DLA Person Year
Saved
93
Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million
17
Quantitative Benefits

Six misconceptions about data
quality
1. You can fix the data
2. Data quality is an IT problem
3. The problem is in the data sources or data entry
4. The data warehouse will provide a single version of
the truth
5. The new system will provide a single version of the
truth
6. Standardization will eliminate the problem of
different "truths" represented in the reports or
analysis
18

The Blind Men and
the Elephant
• It was six men of Indostan, To learning much inclined,
Who went to see the Elephant
(Though all of them were blind),
That each by observation
Might satisfy his mind.
• The First approached the Elephant,
And happening to fall
Against his broad and sturdy side,
At once began to bawl:
"God bless me! but the Elephant
Is very like a wall!"
• The Second, feeling of the tusk
Cried, "Ho! what have we here,
So very round and smooth and sharp? To me `tis mighty clear
This wonder of an Elephant
Is very like a spear!"
• The Third approached the animal,
And happening to take
The squirming trunk within his hands, Thus boldly up he spake:
"I see," quoth he, "the Elephant
Is very like a snake!"
• The Fourth reached out an eager hand, And felt about the knee:
"What most this wondrous beast is like Is mighty plain," quoth he;
"'Tis clear enough the Elephant
Is very like a tree!"
• The Fifth, who chanced to touch the ear, Said: "E'en
the blindest man
Can tell what this resembles most;
Deny the fact who can,
This marvel of an Elephant
Is very like a fan!"
• The Sixth no sooner had begun
About the beast to grope,
Than, seizing on the swinging tail
That fell within his scope.
"I see," quoth he, "the Elephant
Is very like a rope!"
• And so these men of Indostan
Disputed loud and long,
Each in his own opinion
Exceeding stiff and strong,
Though each was partly in the right,
And all were in the wrong!
(Source: John Godfrey Saxe's ( 1816-1887) version of the famous Indian legend ) 19

No universal conception of data
quality exists, instead many differing
perspective compete.
• Problem:
–Most organizations approach
data quality problems in the same way
that the blind men approached the elephant - people
tend to see only the data that is in front of them
–Little cooperation across boundaries, just as the blind
men were unable to convey their impressions about the
elephant to recognize the entire entity.
–Leads to confusion, disputes and narrow views
• Solution:
–Data quality engineering can help achieve a more
complete picture and facilitate cross boundary
communications
20

Structured Data Quality Engineering
1. Allow the form of the
Problem to guide the
form of the solution
2. Provide a means of
decomposing the problem
3. Feature a variety of tools
simplifying system understanding
4. Offer a set of strategies for evolving a design solution
5. Provide criteria for evaluating the quality of the
various solutions
6. Facilitate development of a framework for developing
organizational knowledge.
21

Polling Question #1
22
• Does your organization address or plan to address
data/information quality issues
• Responses
– A. We did last year (2012)
– B. We are this year (2013)
– C. We will next year (2014)
– D. We hope to next year (2014)

6. DDE Tools
Outline
23
Tweetingnow:
#dataed

Mizuho Securities
• Wanted to sell 1 share for
600,000 yen
• Sold 600,000 shares for 1
yen
• $347 million loss
• In-house system did not
have limit checking
• Tokyo stock exchange
system did not have limit
checking ...
• … and doesn't allow order
cancellations
CLUMSY typing cost a Japanese bank at
least £128 million and staff their Christmas
bonuses yesterday, after a trader
mistakenly sold 600,000 more shares than
he should have. The trader at Mizuho
Securities, who has not been named, fell
foul of what is known in financial circles as
“fat finger syndrome” where a dealer types
incorrect details into his computer. He
wanted to sell one share in a new telecoms
company called J Com, for 600,000 yen
(about £3,000).
Infamous Data Quality Example
24

Four ways to make your data sparkle!
1.Prioritize the task
– Cleaning data is costly and time
consuming
– Identify mission critical/non-mission
critical data
2.Involve the data owners
– Seek input of business units on what constitutes "dirty"
data
3.Keep future data clean
– Incorporate processes and technologies that check every
zip code and area code
4.Align your staff with business
– Align IT staff with business units
(Source: CIO JULY 1 2004)
25

• Deming cycle
• "Plan-do-study-act" or
"plan-do-check-act"
1. Identifying data issues that are
critical to the achievement of
business objectives
2. Defining business
requirements for data quality
3. Identifying key data quality
dimensions
4. Defining business rules critical
to ensuring high quality data
26
The DQE Cycle

The DQE Cycle: (1) Plan
• Plan for the assessment of
the current state and
identification of key metrics
for measuring quality
• The data quality engineering
team assesses the scope of
known issues
– Determining cost and impact
– Evaluating alternatives for
addressing them
27

The DQE Cycle: (2) Deploy
28
• Deploy processes for
measuring and improving
the quality of data:
• Data profiling
– Institute inspections and
monitors to identify data
issues when they occur
– Fix flawed processes that are
the root cause of data errors
or correct errors downstream
– When it is not possible to
correct errors at their source,
correct them at their earliest
point in the data flow

The DQE Cycle: (3) Monitor
• Monitor the quality of data
as measured against the
defined business rules
• If data quality meets
defined thresholds for
acceptability, the
processes are in control
and the level of data
quality meets the
business requirements
• If data quality falls below
acceptability thresholds,
notify data stewards so
they can take action
during the next stage
29

The DQE Cycle: (4) Act
• Act to resolve any
identified issues to
improve data quality
and better meet
business
expectations
• New cycles begin as
new data sets come
under investigation
or as new data
quality requirements
are identified for
existing data sets
30

DQE Context & Engineering Concepts
• Can rules be implemented stating that no data can be
corrected unless the source of the error has been
discovered and addressed?
• All data must
be 100%
perfect?
• Pareto
– 80/20 rule
– Not all data
is of equal
Importance
• Scientific,
economic,
social, and
practical
knowledge
31

Data quality is now acknowledged as a major source
of organizational risk by certified risk professionals!
32

6. DDE Tools
Outline
33

Two Distinct Activities Support Quality Data
34
• Data quality best practices depend on both
– Practice-oriented activities
– Structure-oriented activities
Practice-oriented
activities focus on the
capture and
manipulation of data
Structure-oriented
activities focus on the
data implementation
Quality
Data

Practice-Oriented Activities
35
• Stem from a failure to rigor when capturing/manipulating data such as:
– Edit masking
– Range checking of input data
– CRC-checking of transmitted data
• Affect the Data Value Quality and Data Representation Quality
• Examples of improper practice-oriented activities:
– Allowing imprecise or incorrect data to be collected when requirements specify
otherwise
– Presenting data out of sequence
• Typically diagnosed in bottom-up manner: find and fix the resulting
problem
• Addressed by imposing more rigorous data-handling governance
Quality of Data
Representation
Quality of Data
Values
Practice-oriented activities

Structure-Oriented Activities
36
• Occur because of data and metadata that has been arranged imperfectly. For
example:
– When the data is in the system but we just can't access it;
– When a correct data value is provided as the wrong response to a query; or
– When data is not provided because it is unavailable or inaccessible to the customer
• Developer focus within system boundaries instead of within organization boundaries
• Affect the Data Model Quality and Data Architecture Quality
• Examples of improper structure-oriented activities:
– Providing a correct response but incomplete data to a query because the user did not
comprehend the system data structure
– Costly maintenance of inconsistent data used by redundant systems
• Typically diagnosed in top-down manner: root cause fixes
• Addressed through fundamental data structure governance
Quality of
Data Architecture
Quality of
Data Models
Structure-oriented activities

Quality Dimensions
37

A congratulations
letter from another
bank
Problems
• Bank did not know it
made an error
• Tools alone could not
have prevented this error
• Lost confidence in the
ability of the bank to
manage customer funds
38

4 Dimensions of Data Quality
39
An organization’s overall data quality is a function of four distinct
components, each with its own attributes:
• Data Value: the quality of data as stored & maintained in the
system
• Data Representation – the quality of representation for stored
values; perfect data values stored in a system that are
inappropriately represented can be harmful
• Data Model – the quality of data logically representing user
requirements related to data entities, associated attributes, and
their relationships; essential for effective communication among
data suppliers and consumers
• Data Architecture – the coordination of data management
activities in cross-functional system development and operations
Practice-
oriented
Structure-
oriented

Effective Data Quality Engineering
40
Data
Representation
Quality
As presented to
the user
Data Value
Quality
As maintained in
the system
Data Model
Quality
As understood by
developers
Data Architecture
Quality
As an
organizational
asset
(closer to the architect)(closer to the user)
• Data quality engineering has been focused on
operational problem correction
– Directing attention to practice-oriented data imperfections
• Data quality engineering is more effective when also
focused on structure-oriented causes
– Ensuring the quality of shared data across system boundaries

Full Set of Data Quality Attributes
41

Difficult to obtain leverage at the bottom of the falls
42

Frozen Falls
43

New York Turns to Big
Data to Solve Big Tree
Problem
• NYC
– 2,500,000 trees
• 11-months from 2009 to 2010
– 4 people were killed or seriously injured by falling tree limbs in
Central Park alone
• Belief
– Arborists believe that pruning and otherwise maintaining trees
can keep them healthier and make them more likely to withstand
a storm, decreasing the likelihood of property damage, injuries
and deaths
• Until recently
– No research or data to back it up
44
http://www.computerworld.com/s/article/9239793/New_York_Turns_to_Big_Data_to_Solve_Big_Tree_Problem?source=CTWNLE_nlt_datamgmt_2013-06-05

NYC's Big Tree Problem
• Question
– Does pruning trees in one year reduce the
number of hazardous tree conditions in the
following year?
• Lots of data but granularity challenges
– Pruning data recorded block by block
– Cleanup data recorded at the address level
– Trees have no unique identifiers
• After downloading, cleaning, merging, analyzing and intensive
modeling
– Pruning trees for certain types of hazards caused a 22 percent reduction in the
number of times the department had to send a crew for emergency cleanups
• The best data analysis
– Generates further questions
• NYC cannot prune each block every year
– Building block risk profiles: number of trees, types of trees, whether the block
is in a flood zone or storm zone
45
http://www.computerworld.com/s/article/9239793/New_York_Turns_to_Big_Data_to_Solve_Big_Tree_Problem?source=CTWNLE_nlt_datamgmt_2013-06-05

6. DDE Tools
Outline
46

Letter from the Bank
… so please continue to open your
mail from either Chase or Bank One
P.S. Please be on the lookout for any
upcoming communications from
either Chase or Bank One regarding
your Bank One credit card and any
other Bank One product you may
have.
Problems
• I initially discarded the letter!
• I became upset after reading it
• It proclaimed that Chase has data
quality challenges
47

Polling Question #2
48
• Does your organization utilize a structured or formal
approach to information quality?
• A. Yes
• B. They say they are but they aren't
• C. No

6. DDE Tools
Outline
49

Data acquisition activities Data usage activitiesData storage
Traditional Quality Life Cycle
50

restored data
Metadata
Creation
Metadata Refinement
Metadata
Structuring
Data Utilization
Data Manipulation
Data Creation
Data Storage
Data
Assessment
Data
Refinement
51
data
architecture
& models
populated data
models and
storage locations
data values
data
values
data
values
value
defects
structure
defects
architecture
refinements
model
refinements
Data Life
Cycle
Model
Products
data

restored data
Metadata Refinement
Metadata
Structuring
Data Utilization
Data Manipulation
Data Creation
Data Storage
Data
Assessment
Data
Refinement
52
populated data
models and
storage locations
data
values
Data Life
Cycle
Model:
Quality
Focus
data
architecture &
model quality
model quality
value quality
value quality
value quality
representation
quality
Metadata
Creation
architecture
quality

Starting
point
for new
system
development
data performance metadata
data architecture
data
architecture and
data models
shared data updated data
corrected
data
architecture
refinements
facts &
meanings
Metadata &
Data Storage
Starting point
for existing
systems
Metadata Refinement
• Correct Structural Defects
• Update Implementation
Metadata Creation
• Define Data Architecture
• Define Data Model Structures
Metadata Structuring
• Implement Data Model Views
• Populate Data Model Views
Data Refinement
• Correct Data Value Defects
• Re-store Data Values
Data Manipulation
• Manipulate Data
• Updata Data
Data Utilization
• Inspect Data
• Present Data
Data Creation
• Create Data
• Verify Data Values
Data Assessment
• Assess Data Values
• Assess Metadata
Extended data life cycle model with metadata sources and uses
53

Polling Question #3
54
• Do you use metadata models, modeling tools, or
profiling to support your information quality efforts?
• A. Yes
• B. No

6. DDE Tools
Outline
55

Profile, Analyze and Assess DQ
• Data assessment using 2 different approaches:
– Bottom-up
– Top-down
• Bottom-up assessment:
– Inspection and evaluation of the data sets
– Highlight potential issues based on the
results of automated processes
• Top-down assessment:
– Engage business users to document
their business processes and the
corresponding critical data dependencies
– Understand how their processes
consume data and which data elements
are critical to the success of the business
applications
56

Define DQ Measures
• Measures development occurs as part of the strategy/
design/plan step
• Process for defining data quality measures:
1. Select one of the identified critical business impacts
2. Evaluate the dependent data elements, create and update
processes associate with that business impact
3. List any associated data requirements
4. Specify the associated dimension of data quality and one or
more business rules to use to determine conformance of the
data to expectations
5. Describe the process for measuring conformance
6. Specify an acceptability threshold
57

Set and Evaluate DQ Service Levels
• Data quality inspection and
monitoring are used to
measure and monitor
compliance with defined
data quality rules
• Data quality SLAs specify
the organization’s expectations for response and remediation
• Operational data quality control defined in data quality SLAs
includes:
– Data elements covered by the agreement
– Business impacts associated with data flaws
– Data quality dimensions associated with each data element
– Quality expectations for each data element of the identified dimensions in
each application for system in the value chain
– Methods for measuring against those expectations
– (…)
58

Measure, Monitor & Manage DQ
• DQM procedures depend on
available data quality measuring
and monitoring services
• 2 contexts for control/measurement
of conformance to data quality
business rules exist:
– In-stream: collect in-stream measurements while creating data
– In batch: perform batch activities on collections of data
instances assembled in a data set
• Apply measurements at 3 levels of granularity:
– Data element value
– Data instance or record
– Data set
59

Overview: Data Quality Tools
4 categories of
activities:
1) Analysis
2) Cleansing
3) Enhancement
4) Monitoring
60
Principal tools:
1) Data Profiling
2) Parsing and Standardization
3) Data Transformation
4) Identity Resolution and
Matching
5) Enhancement
6) Reporting

DQ Tool #1: Data Profiling
• Data profiling is the assessment of
value distribution and clustering of
values into domains
• Need to be able to distinguish
between good and bad data before
making any improvements
• Data profiling is a set of algorithms
for 2 purposes:
– Statistical analysis and assessment of the data quality values within a
data set
– Exploring relationships that exist between value collections within and
across data sets
• At its most advanced, data profiling takes a series of prescribed
rules from data quality engines. It then assesses the data,
annotates and tracks violations to determine if they comprise
new or inferred data quality rules
61

DQ Tool #1: Data Profiling, cont’d
• Data profiling vs. data quality-business context and
semantic/logical layers
– Data quality is concerned with proscriptive rules
– Data profiling looks for patterns when rules are adhered to and when
rules are violated; able to provide input into the business context layer
• Incumbent that data profiling services notify all concerned
parties of whatever is discovered
• Profiling can be used to…
– …notify the help desk that valid
changes in the data are about to
case an avalanche of “skeptical
user” calls
– …notify business analysts of
precisely where they should be
working today in terms of shifts
in the data
62

Courtesy GlobalID.com
63

DQ Tool #2: Parsing & Standardization
• Data parsing tools enable the definition
of patterns that feed into a rules engine
used to distinguish between valid
and invalid data values
• Actions are triggered upon matching
a specific pattern
• When an invalid pattern is recognized,
the application may attempt to
transform the invalid value into one that meets expectations
• Data standardization is the process of conforming to a set of
business rules and formats that are set up by data stewards
and administrators
• Data standardization example:
– Brining all the different formats of “street” into a single format, e.g.
“STR”, “ST.”, “STRT”, “STREET”, etc.
64

DQ Tool #3: Data Transformation
• Upon identification of data errors, trigger data rules to
transform the flawed data
• Perform standardization and guide rule-based
transformations by mapping data values in their original
formats and patterns into a target representation
• Parsed components of a pattern are subjected to
rearrangement, corrections, or any changes as directed
by the rules in the knowledge base
65

DQ Tool #4: Identify Resolution & Matching
• Data matching enables analysts to identify relationships between records for
de-duplication or group-based processing
• Matching is central to maintaining data consistency and integrity throughout
the enterprise
• The matching process should be used in
the initial data migration of data into a
single repository
• 2 basic approaches to matching:
• Deterministic
– Relies on defined patterns/rules for assigning
weights and scores to determine similarity
– Predictable
– Dependent on rules developers anticipations
• Probabilistic
– Relies on statistical techniques for assessing the probability that any pair of record
represents the same entity
– Not reliant on rules
– Probabilities can be refined based on experience -> matchers can improve precision as
more data is analyzed
66

DQ Tool #5: Enhancement
• Definition:
– A method for adding value to information by accumulating additional
information about a base set of entities and then merging all the sets of
information to provide a focused view. Improves master data.
• Benefits:
– Enables use of third party data sources
– Allows you to take advantage of the information and research carried
out by external data vendors to make data more meaningful and useful
• Examples of data enhancements:
– Time/date stamps
– Auditing information
– Contextual information
– Geographic information
– Demographic information
– Psychographic information
67

DQ Tool #6: Reporting
• Good reporting supports:
– Inspection and monitoring of conformance to data quality expectations
– Monitoring performance of data stewards conforming to data quality
SLAs
– Workflow processing for data quality incidents
– Manual oversight of data cleansing and correction
• Data quality tools provide dynamic reporting and monitoring
capabilities
• Enables analyst and data stewards to support and drive the
methodology for ongoing DQM and improvement with a
single, easy-to-use solution
• Associate report results with:
– Data quality measurement
– Metrics
– Activity
68

6. DDE Tools
Outline
69

• Develop and promote data quality awareness
• Define data quality requirements
• Profile, analyze and asses data quality
• Define data quality metrics
• Define data quality business
rules
• Test and validate data quality
requirements
• Set and evaluate data quality
service levels
• Measure and monitor data quality
• Manage data quality issues
• Clean and correct data quality defects
• Design and implement operational DQM procedures
• Monitor operational DQM procedures and performance
Overview: DQE Concepts and Activities
70

Concepts and Activities
• Data quality expectations provide the inputs necessary
to define the data quality framework:
– Requirements
– Inspection policies
– Measures, and monitors
that reflect changes in data
quality and performance
• The data quality framework
requirements reflect 3 aspects
of business data expectations
1. A manner to record the expectation in business rules
2. A way to measure the quality of data within that dimension
3. An acceptability threshold
71
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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References & Recommended Reading
75
• The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• http://www2.sas.com/proceedings/sugi29/098-29.pdf

Data Quality Dimensions
76

Data Value Quality
77

Data Representation Quality
78

Data Model Quality
79

Data Architecture Quality
80

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