Data-Ed Webinar: Data Quality Success Stories

Data Quality Success Stories: 
Unlock Business Value through Data Quality Engineering
Copyright 2015 by Data Blueprint Slide # 1
Peter Aiken, Ph.D. & Karen Akens
Peter Aiken, Ph.D.
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2Copyright 2015 by Data Blueprint Slide #

Karen Akens
• Certified Data Management
Professional
• Data Consultant with Data
Blueprint
• Business and Technical
Requirements Focused
• Data Quality and Data
Governance Concentration
• Board Member DAMA-
Central Virginia
1. Data Quality In Context
2. DQE Definitions (w/ example)
3. DQE Cycle
4. Solution Approach
5. DQ Causes, Dimensions, Life Cycle
6. Calculating Business Value
7. Takeaways and Q&A
Tweeting now:
#dataed

We believe ...
Data  
Assets
Financial  
Assets
Real 
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be  
used up
Can be  
used up
Non-
degrading √ √ Can degrade 
over time
Can degrade 
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depleteable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or transactions and from which
future economic benefits are expected to flow [Wikipedia]
 
 
 
UsesUsesReuses
What is data management?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
Specialized Team Skills
Data Governance
Understanding the current
and future data needs of an
enterprise and making that
data effective and efficient in
supporting  
business activities  
Aiken, P, Allen, M. D., Parker, B., Mattia, A.,  
"Measuring Data Management's Maturity:  
A Community's Self-Assessment"  
IEEE Computer (research feature April 2007)
Data management practices connect
data sources and uses in an
organized and efficient manner
• Engineering
• Storage
• Delivery
• Governance
When executed,  
engineering, storage, and  
delivery implement governance
Note: does not well-depict data reuse

What is data management?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
 
Resources 
(optimized for reuse) 
Data Governance
AnalyticInsight
Maslow's Hierarchiy of Needs

You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk 
(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality

Copyright 2013 by Data Blueprint
The DAMA Guide to the Data Management Body of Knowledge
11
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
• Organized around
several environmental
elements
3. DQE Cycle
Tweeting now:
#dataed

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

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 ) 15
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
16

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.
17
Data quality principles
Principle Implications
1. Capture data right, first time
Wherever possible all data is captured once, at source, and
validated on input
2. ‘Engineer-in’ positive impacts
on data quality
Wherever possible data quality improvement is automated, proactive
and on-going
Systems, processes and products are inherently designed to improve
data quality. e.g.
• The possibility of errors when data is entered or changed is
‘engineered out’
• Processes are designed to enter and maintain accurate data
• Data entry is quick and intuitive for users
3. Integrate data quality into
business processes
Data quality standards and rules are defined and integrated into
day-to-day operations e.g. instances of non-compliance are fixed
at root cause
There is clear accountability throughout our organization for
promoting & sustaining good quality data
Every work stream has a part to play if we are to move  
from a reactive to a proactive approach to improving data quality

3. DQE Cycle
Tweeting now:
#dataed
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)
20

• There is no doubt current data quality issues are costing  
money & creating regulatory & reputational risk
Inconsistencies in payment terms vendor
master & vendor sites mean 30% of
suppliers are on immediate payment
terms instead of 30-60 days.
Remittance Email addresses are
missing in 88% of cases, as a
result payments have to be sent by
post
68% of vendor phone numbers were missing /
in the wrong format
90% of suppliers are on immediate payment terms
40 workdays per year are spent tracking down
missing vendor information to avoid tax penalties
Lack of automated processes for deactivating
vendors resulting in 222,000 obsolete vendors
removed during profiling, whereas many
systems still contain ROT.
If you want to avoid situations like this…
A data quality
framework which
provides clarity on
materiality, coverage,
and outcomes, and
can assign actions and
owners, and monitor
resolution
Data quality principles
that are embedded in
process & system
design across the
enterprise
An organizational
culture that treats data
as a strategic asset
A data quality COE
A Data Governance
Board with a mandate  
to drive data quality  
enterprise-wide
A Master Data
Management solution
…you need to have this…
A data governance
framework which
articulates the roles of
data owners, data
custodians & data
stewards

Discovery - Identify potential issues.
Profile Data - Review sample data and
existing data creation and usage process to
provide context for business rule discussion
with Data Owners and Business Stewards.
Develop Business Rules - Work with Data
Owners and Business Data Stewards to
review documented business rules and
capture undocumented rules.
Define Metrics - Define metrics and
acceptable thresholds against which to
measure levels of quality.
Evaluate Data with Metrics - Execute
business rules against production data and
evaluate results. Utilize acceptable thresholds
set by the Data Governance Board to evaluate
the data.
Findings Review - Review the Findings with
the Data Owners and Business Data
Stewards.
Remediate Anomalies - Implement and
execute remediation process to fix problems
with production data.
Monitor Health - Define and implement a
continuous monitoring/remediation plan to
prevent and/or fix data quality problems in the
future.
Repeatable Process
3. DQE Cycle
Tweeting now:
#dataed

Identifying Business Need & Resources
• Discovery process – not solely the responsibility of
business, IT, or Data Governance/Data Quality
organizations. Requires collaboration.
• Business need or problem definitions can be influenced
by a variety of sources such as:
Migrating to a single ERP and CRM
Master Data Management Processes
Suspected data quality deficiencies impacting BV & regulatory requirements
Data Governance Board initiatives
Needs of data-centric business strategies and opportunities
Directives from executive sponsorship team
Identifying Business Need & Resources

Identifying Business Need & Business Resources
Business
Data
Steward
Data
Owner
• Accountable for Data Quality
• Authority to Grant Access to Data
• Understands how Data is used in Business Process
• Articulates Business Rules
• Participant in Data Cleansing Process
Identifying Business Need & Resources  
Data Quality Center of Excellence and IT Resources
• Provides Physical Access to Data
• Understands Data Linkages
• Can Identify Archiving/Deletion Process
• Leads Data Quality Effort
• Analytical and Technical Skills to use IDQ
• Provides Findings Briefs to Data Owners and Data
Stewards
IT Data
Steward
Data Quality
Analyst

3. DQE Cycle
Tweeting now:
#dataed
Two Distinct Activities Support Quality Data
30
• 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
31
• 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
32
• 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
33
4 Dimensions of Data Quality
34
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
35
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
36

Difficult to obtain leverage at the bottom of the falls
37
Frozen Falls
38

Data acquisition activities Data usage activitiesData storage
Traditional Quality Life Cycle
39
restored data
 
Metadata  
Creation
 
Metadata Refinement
 
 
Metadata
Structuring
 
Data Utilization
 
Data Manipulation
 
 
 
Data Creation
Data Storage
 
 
Data
Assessment
 
 
Data  
Refinement
40
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

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
41
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
42
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
43
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
3. DQE Cycle
Tweeting now:
#dataed

Defining Business Rules and Metrics 
What is a Data Quality Business Rule?
Each business rule should be reviewed and the data quality dimensions it
addresses should be identified.
Specific characteristics that can be measured should be associated with each rule
and thresholds of acceptable performance should be established.
If the data domain falls under global standards, acceptable thresholds will be
established by the Data Governance Board.
Additional thresholds may also be agreed upon at the local level.
Defining Business Rules and Metrics

What Makes “Good Metrics”?
Examples of Metrics for Various Dimensions
• Does each value fall within an allowed set of values?
• Does each value conform to the defined level of precision?
Accuracy
• Is data present in required fields?Completeness
• Is the data used the same way across the enterprise?Consistency
• Is the data up to date?Currency
• Are identifying data elements unique?Integrity
• Are data elements stored as assigned data types, e.g. is text
stored in a telephone number field?Conformity
• Do duplicate records exist?Duplication

Business Value from Data Quality
• The costs of poor data quality include:
• Human capital expense for manual correction
• Revenue lost due to inaccurate information
• Regulatory fines from compliance violations
• Damage to corporate reputation
# Errors
Identified
Potential Cost
Avoidance
Business Rule: Customer Address Invalid 84367 92,952.42$
Calculation Description:
Manual effot to research and correct an invalid Customer
Address
Average Salary for worker engaged in correcting address 25,000.00$
Average Salary including benefits 34,375.00$
Salary per hour 16.53$
Salary per minute 0.28$
# minutes to correct an invalid address 4
Cost of manual effort to research and correct one address: 1.10$
Business Value Calculations Reporting

3. DQE Cycle
Tweeting now:
#dataed
3. DQE Cycle
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
52

Questions?
+ =
Design & Manage Data Structures
w/ Dave Marsh
October 13, 2015 @ 2:00 PM ET/11:00 AM PT
Metadata Strategies
November 10, 2015 @ 2:00 PM ET/11:00 AM PT
Sign up here:
www.datablueprint.com/webinar-schedule
or www.dataversity.net
Upcoming Events
54

References & Recommended Reading
55
• 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
56

Data Value Quality
57
Data Representation Quality
58

Data Model Quality
59
Data Architecture Quality
60

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Glen Allen, Virginia 23060
804.521.4056
Copyright 2015 by Data Blueprint Slide # 61

Data-Ed Webinar: Data Quality Success Stories

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