Data-Ed Engineering Solutions to Data Quality Challenges

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 Data Creation
architecture and
data models

facts &
Metadata &
Data Storage
data performance metadata
Data Assessment
meanings

Time: 2:00 PM ET
• Create Data • Assess Data Values
• Verify Data Values • Assess Metadata

shared data updated data
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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BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 10/09/12 1
10/04/12 © Copyright this and previous years by Data Blueprint - all rights reserved!

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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
3 - datablueprint.com 10/11/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Data Quality
Engineering

DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 10/09/12

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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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
PRODUCED

TITLE
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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TITLE
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
#dataed
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© Copyright this and previous years by Data Blueprint - all rights reserved!

TITLE
Data Management

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1/26/2010

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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1/26/2010

TITLE
Data Management

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TITLE
Overview: Data Quality Engineering

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
PRODUCED
1/26/2010

TITLE
Overview: Data Quality Engineering

PRODUCED
1/26/2010

TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

PRODUCED

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
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10/09/12 15

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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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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TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

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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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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
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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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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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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
PRODUCED

TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

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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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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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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
PRODUCED

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
PRODUCED

TITLE
Data Quality Dimensions

PRODUCED

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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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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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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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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
PRODUCED

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
PRODUCED

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
PRODUCED

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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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

PRODUCED

TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

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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
PRODUCED

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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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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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
Data
Quality
of
Data

Values Representa2on

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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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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

PRODUCED
10/09/12 44

TITLE
Effective Data Quality Engineering
• 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
(closer
to
the
user) (closer
to
the
architect)

Data
Data
Value

Data
Model
Data
Architecture

Representa9on
Quality
Quality Quality
Quality
As
an

As
understood
by
organiza9onal

As
presented
to
As
maintained
in

developers asset
the
user the
system

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TITLE
Full Set of Data Quality Attributes

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TITLE
Data Value Quality

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TITLE
Data Representation Quality

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TITLE
Data Model Quality

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TITLE
Data Architecture Quality

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TITLE
Extended data life cycle model with metadata sources and uses
Starting
point Metadata Refinement
Metadata Creation
for new • Define Data Architecture • Correct Structural Defects
system • Update Implementation
• Define Data Model Structures
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
architecture and Metadata &
data models Data Storage
data performance metadata
Data Creation facts & Data Assessment
• Create Data meanings • Assess Data Values
• Verify Data Values • Assess Metadata

shared data updated data
Starting point
for existing
Data Utilization Data Manipulation systems
• Inspect Data • Manipulate Data
• Present Data • Updata Data

PRODUCED

TITLE

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ü ü ü ü ü ü ü

PRODUCED
1/26/2010

Goals and Principles
TITLE

§ To measurably improve the
quality of data in relation to
defined business expectations
§ To define requirements and
specifications for integrating
data quality control into the
system development life cycle
§ To provide defined processes for measuring,
monitoring, and reporting conformance to
acceptable levels of data quality
PRODUCED
1/26/2010

TITLE
Activities
• Develop and Promote Data Quality Awareness
• Set and Evaluate Data Quality Service Levels
• Test and Validate Data Quality Requirements
• Profile, Analyze, and Assess Data Quality
• Continuously Measure and Monitor Data Quality
• Monitor Operational DQM Procedures and Performance
• Define Data Quality Business Rules
• Define Data Quality Metrics
• Manage Data Quality Issues
• Clean and Correct Data Quality Defects
• Define Data Quality Requirements
• Design and Implement Operational DQM Procedures
PRODUCED
1/26/2010

TITLE
Primary Deliverables
• Improved Quality Data
• Data Management
Operational Analysis
• Data profiles
• Data Quality Certification
Reports
• Data Quality Service
Level Agreements
PRODUCED
1/26/2010

TITLE

Roles and Responsibilities
Suppliers:
§ External Sources
§ Regulatory Bodies
§ Business Subject Matter
Experts
§ Information Consumers
§ Data Producers
§ Data Architects
§ Data Modelers
§ Data Stewards
Participants: Consumers:
§ Data Quality Analysts § Data Stewards
§ Data Analysts § Data Professionals
§ Database Administrators § Other IT Professionals
§ Data Stewards § Knowledge Workers
§ Other Data Professionals § Managers and
§ DRM Director Executives
§ Data Stewardship Council § Customers
PRODUCED
1/26/2010

TITLE
Polling Question #2
What is one guiding principle for data quality?
a. Business
process
owners
will

agree
to
and
abide
by
data

quality
SLAs

a. IdenDfy
a
blue
record
for
all

data
elements

a. Upstream
data
consumers

speciﬁc
data
quality

expectaDons

PRODUCED

TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

PRODUCED

TITLE
Technology
• Data Profiling Tools
• Statistical Analysis Tools
• Data Cleansing Tools
• Data Integration Tools
• Issue and Event Management Tools

PRODUCED

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

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TITLE
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
PRODUCED

TITLE
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
PRODUCED

TITLE
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.
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TITLE
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
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TITLE
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
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TITLE
DQ Tool #5: Enhancement
Definition: Examples of data
• A method for adding value to enhancements:
information by accumulating • Time/date stamps
additional information about a • Auditing information
base set of entities and then
merging all the sets of • Contextual information
information to provide a focused • Geographic information
view. Improves master data. • Demographic information
Benefits: • Psychographic information
• 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

PRODUCED

TITLE
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
PRODUCED

TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

PRODUCED

Guiding Principles
TITLE

1) Manage data as a core organizational asset.
2) Identify a gold record for all data elements
3) All data elements will have a standardized data definition, data type, and
acceptable value domain
4) Leverage data governance for the control and performance of DQM
5) Use industry and international data standards whenever possible
6) Downstream data consumers specify data quality expectations
7) Define business rules to assert conformance to data quality expectations
8) Validate data instances and data sets against defined business rules
9) Business process owners will agree to and abide by data quality SLAs
10) Apply data corrections at the original source if possible
11) If it is not possible to correct data at the source, forward data corrections to
the owner of the original source. Influence on data brokers to conform to
local requirements may be limited
12) Report measured levels of data quality to appropriate data stewards,
business process owners, and SLA managers

PRODUCED

TITLE
Interdependencies - Tools alone cannot do the job!
Education and Training
(People)

Data Cleansing and Prevention Data Quality Tools
(Process) (Technology)
PRODUCED

TITLE
Summary: Data Quality Engineering

PRODUCED
1/26/2010

TITLE
Outline


3. DQM Cycle


5. DQ Dimensions


Tweeting now:

PRODUCED

TITLE
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Data-Ed Engineering Solutions to Data Quality Challenges