More Related Content Similar to Data-Ed Online: Engineering Solutions to Data Quality Challenges (20) More from Data Blueprint (20) Data-Ed Online: Engineering Solutions to Data Quality Challenges1. Data Quality Engineering
TITLE
This presentation provides guidance to
organizations considering data quality initiatives
or preparing for data quality initiatives. This talk
will illustrate how organizations with chronic
business challenges often can trace the root of
the problem to poor data quality. Showing how
data quality can be engineered provides a
useful framework in which to develop an
organizational approach. This in turn will allow
organizations to more quickly identify data
problems caused by structural issues versus
practice-oriented defects. Participants will also Starting
learn the importance of practicing data quality point
for new
system
Metadata Creation
• Define Data Architecture
• Define Data Model Structures
Metadata Refinement
• Correct Structural Defects
• Update Implementation
engineering quantification.
development
architecture
data architecture
refinements
Metadata Structuring Data Refinement
• Implement Data Model Views • Correct Data Value Defects
• Populate Data Model Views corrected • Re-store Data Values
data
data
Date: October 9, 2012
architecture and Metadata &
data models Data Storage
data performance metadata
Data Creation facts & Data Assessment
• Create Data meanings • Assess Data Values
Time: 2:00 PM ET • Verify Data Values
shared data updated data
• Assess Metadata
Starting point
for existing
Presented by: Dr. Peter Aiken
Data Utilization Data Manipulation systems
• Inspect Data • Manipulate Data
• Present Data • Updata Data
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Commonly Asked Questions
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Commonly Asked Questions
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Commonly Asked Questions
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5. Get Social With Us!
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7. Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized thought-
leader in the data management
field - 30 years of experience
– Recipient of multiple international
awards
– Founder, Data Blueprint
(http://datablueprint.com)
• 7 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, Deutsche Bank, Nokia,
Wells Fargo, the Commonwealth
of Virginia and Walmart
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8. Data Quality
Engineering
Data Quality Engineering
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 10/09/12
9. Data Quality
Engineering
Data Quality Engineering
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 10/09/12
10. Data Quality
Engineering
Data Quality Engineering
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 10/09/12
11. Data Quality
Engineering
Data Quality Engineering
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 10/09/12
12. TITLE
Outline
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Outline
1. Data Management Introduction
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Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
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1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
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Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
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Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
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Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
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Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
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Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
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21. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
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The DAMA Guide to the Data Management Body of Knowledge
Data
Management
Functions
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The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
Data
Management
Functions
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The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
Data
Management
Functions
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The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
Data
Management
Functions
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26. TITLE
The DAMA Guide to the Data Management Body of Knowledge
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
Data
Management
Functions
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The DAMA Guide to the Data Management Body of Knowledge
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
Data
Management
Functions
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28. TITLE
The DAMA Guide to the Data Management Body of Knowledge
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
Data
Management
Functions
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The DAMA Guide to the Data Management Body of Knowledge
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The DAMA Guide to the Data Management Body of Knowledge
Environmental Elements
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The DAMA Guide to the Data Management Body of Knowledge
Amazon:
http://
www.amazon.com/
DAMA-Guide-
Management-
Knowledge-DAMA-
DMBOK/dp/
0977140083
Or enter the terms
"dama dm bok" at the
Amazon search
engine
Environmental Elements
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What is the 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
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Data Management
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34. TITLE
Data Management
Data Program
Coordination
Organizational
Data Integration
Data Stewardship Data Development
Data Support
Operations
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Data Management
Manage data coherently.
Data Program
Coordination
Organizational
Data Integration
Data Stewardship Data Development
Data Support
Operations
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36. TITLE
Data Management
Manage data coherently.
Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration
Data Stewardship Data Development
Data Support
Operations
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37. TITLE
Data Management
Manage data coherently.
Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration
Data Stewardship Data Development
Assign responsibilities for data.
Data Support
Operations
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38. TITLE
Data Management
Manage data coherently.
Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration
Data Stewardship Data Development
Assign responsibilities for data.
Engineer data delivery systems.
Data Support
Operations
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39. TITLE
Data Management
Manage data coherently.
Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration
Data Stewardship Data Development
Assign responsibilities for data.
Engineer data delivery systems.
Data Support
Operations
Maintain data availability.
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Data Management
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41. TITLE
Overview: Data Quality Engineering
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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42. TITLE
Overview: Data Quality Engineering
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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43. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
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8. References and Q&A #dataed
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44. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
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8. References and Q&A #dataed
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45. TITLE
Definitions
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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46. TITLE
Definitions
Data Quality Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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47. TITLE
Definitions
Data Quality Management
• Planning, implementation and control activities that
apply quality management techniques to measure,
assess, improve, and ensure the fitness of data for
use
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48. TITLE
Definitions
Data Quality Management
• Planning, implementation and control activities that
apply quality management techniques to measure,
assess, improve, and ensure the fitness of data for
use
• 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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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49. TITLE
Definitions
Data Quality Management
• Planning, implementation and control activities that
apply quality management techniques to measure,
assess, improve, and ensure the fitness of data for
use
• 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 support process in organizational change management
• Continuous process for defining the parameters for specifying
acceptable levels of data quality to meet business needs and for
ensuring that data quality meets these levels
Data Quality
• Synonymous with information quality, since poor data quality results
in inaccurate information and poor business performance
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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50. TITLE
Overview: DQM Concepts and Activities
1) Data Quality Management Approach
2) Develop and promote data quality awareness
3) Define data quality requirements
4) Profile, analyze and assess data quality
5) Define data quality metrics
6) Define data quality business rules
7) Test and validate data quality requirements
8) Set and evaluate data quality service levels
9) Measure and monitor data quality
10) Manage data quality issues
11) Clean and correct data quality defects
12) Design and implement operational DQM procedures
13) Monitor operational DQM procedures and performance
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51. TITLE
Concepts and Activities
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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52. TITLE
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
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53. TITLE
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
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54. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
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8. References and Q&A #dataed
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55. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
Tweeting now:
8. References and Q&A #dataed
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56. TITLE
The DQM Cycle
The general approach to DQM
is a version of the Deming
cycle.
Deming proposes a problem–solving
model known as “plan-do-study-act”
or “plan-do-check-act”
The cycle begins by:
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
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57. TITLE
The DQM Cycle
The general approach to DQM
is a version of the Deming
cycle.
Deming proposes a problem–solving
model known as “plan-do-study-act”
or “plan-do-check-act”
The cycle begins by:
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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58. TITLE
The DQM Cycle: (1) Plan
Plan for the assessment of
the current state and
identification of key metrics
for measuring quality
• The data quality team
assesses the scope of
known issues
• This involves:
– Determining cost and
impact
– Evaluating alternatives for
addressing them
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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59. TITLE
The DQM Cycle: (2) Deploy
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
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60. TITLE
The DQM 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
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61. TITLE
The DQM 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
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62. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
Tweeting now:
8. References and Q&A #dataed
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63. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
Tweeting now:
8. References and Q&A #dataed
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64. TITLE
Develop and Promote DQ Awareness
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65. TITLE
Develop and Promote DQ Awareness
• Promoting data quality awareness is
essential to ensure buy-in of necessary
stakeholders in the organization
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66. TITLE
Develop and Promote DQ Awareness
• Promoting data quality awareness is
essential to ensure buy-in of necessary
stakeholders in the organization
• Ensure that the right people in the
organization are aware of the existence
of data quality issues
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67. TITLE
Develop and Promote DQ Awareness
• Promoting data quality awareness is
essential to ensure buy-in of necessary
stakeholders in the organization
• Ensure that the right people in the
organization are aware of the existence
of data quality issues
• Awareness increases the chance of
success of any DQM program
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68. TITLE
Develop and Promote DQ Awareness
• Promoting data quality awareness is
essential to ensure buy-in of necessary
stakeholders in the organization
• Ensure that the right people in the
organization are aware of the existence
of data quality issues
• Awareness increases the chance of
success of any DQM program
• Awareness includes:
– Relating material impacts to data issues
– Ensuring systematic approaches to
regulators
– Oversight of the quality of organizational
data
– Socializing the concept that data quality
problems cannot be solely addressed by
technology solutions
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69. TITLE
Polling Question #1
Which is not a step to promote data quality
awareness?
a) Training on the core
concepts of data quality
b) Establish data governance
framework for data quality
c) Create a data architecture
map
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70. TITLE
Develop and Promote DQ Awareness: Steps
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71. TITLE
Develop and Promote DQ Awareness: Steps
1) Training on the core
concepts of data quality
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72. TITLE
Develop and Promote DQ Awareness: Steps
1) Training on the core
concepts of data quality
2) Establish data governance
framework for data quality
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73. TITLE
Develop and Promote DQ Awareness: Steps
1) Training on the core
concepts of data quality
2) Establish data governance
framework for data quality
3) Create a data quality
oversight board that has a
reporting hierarchy
associated with the
different data governance
roles
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74. TITLE
Define DQ Requirements
• Data quality must be understood within the context of ‘fitness for
use’
• Data quality requirements are often hidden within defined
business policies
• Incremental detailed review and iterative refinement of business
policies helps to identify those information requirements which
become data quality rules
• Steps for incremental detailed review:
– Identify key data components associated with business policies
– Determine how identified data assertions affect the business
– Evaluate how data errors are categorized within a set of data quality
dimensions
– Specify the business rules that measure the occurrence of data
errors
– Provide a means for implementing measurement processes that
assess conformance to those business rules
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75. TITLE
Data Quality Dimensions
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76. TITLE
Profile, Analyze and Assess DQ
Data assessment using 2 different approaches:
1) Bottom-up
2) 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
application
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77. TITLE
Define DQ Metrics
• Metrics development occurs as part of the
strategy/design/plan step
• Process for defining data quality metrics:
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
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78. TITLE
Test and Validate DQ Requirements
• Data profiling tools
analyze data to find
potential anomalies
• Use the same tools
for rule validation
• Rules discovered or defined during the
data quality assessment phase are
referenced in measuring conformance as
part of the operational process
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79. TITLE
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 indentified
dimensions in each application for system in the value chain
– Methods for measuring against those expectations
– (…) from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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80. TITLE
Measure and Monitor 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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81. Clean & Correct
Manage DQ Issues
DQ Defects
• Supporting the enforcement of Perform data correction
the data quality SLA requires a
mechanism for reporting and in 3 ways:
tracking data quality incidents 1) Automated correction
and activities for researching 2) Manual directed correction
and resolving those incidents 3) Manual correction
• A data quality incident
reporting system can provide
this capability
• It can log the evaluation, initial
diagnosis, and actions
associated with data quality
events
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82. Manage DQ Issues: Example
TITLE
Data quality incident tracking focuses on training staff to recognize
when data issues appear and how they are to be classified, logged and
tracked according to the data quality SLA
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83. Design and Implement Monitor Operational
Operational DQM DQM Procedures and
Procedures Performances
1) Inspection and monitoring 1) Accountability is critical
2) Diagnosis and evaluation to governance
of remediation protocols overseeing
alternatives data quality control
3) Resolve issues 2) All issues must be
4) Reporting assigned
3) The tracking process
should specify and
document the ultimate
issue accountability
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84. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
Tweeting now:
8. References and Q&A #dataed
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85. TITLE
Outline
1. Data Management Introduction
2. Data Quality Definitions & Overview
3. DQM Cycle
4. DQ Awareness & Requirements
5. DQ Dimensions
6. Data Quality Tools
7. Guiding Principles
Tweeting now:
8. References and Q&A #dataed
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86. TITLE
Example: Data Quality Interview Session Summary
• During mid-February, the Data Governance Team and Data
Blueprint conducted ten qualitative interview sessions with groups
of individuals who interact with data on regular basis
• A series of patterns emerged as participants shared stories about
the impact of poor data quality on the client, its products, and its
customers
• These patterns highlight gaps in best
practices for ensuring data quality,
i.e. the extent to which data is
“fit for use”
• Our preliminary analysis evaluated
these stories against attributes of four
data quality dimensions
• At this early stage of the post-interview
process, we are seeking confirmation of
our assumptions and method
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87. TITLE
Which Activities Support Quality Data?
• Data quality best practices depend on both
– Practice-oriented activities
– Structure-oriented activities
Quality
Practice-oriented Data Structure-oriented
activities focus on activities focus on
the capture and the data
manipulation of data implementation
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88. TITLE
Quality Dimensions
Practice-oriented causes
• Stem from a failure to rigor when
capturing and manipulating data
such as:
– Edit masking
– Range checking of input data
– CRC-checking of transmitted data
Structure-oriented causes
• 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
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89. TITLE
Practice-Oriented Activities
• 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
Practice-oriented activities
Quality of Quality of
Data Values Data
Representatio
n
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90. TITLE
Structure-Oriented Activities
• 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
Structure-oriented activities
Quality of Quality of
Data Models Data
Architecture
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91. TITLE
4 Dimensions of Data Quality
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
Practice-
oriented
• 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
Structure-
communication among data suppliers and consumers
oriented
• Data Architecture – the coordination of data
management activities in cross-functional system
development and operations
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