Subscribe Sponsorship Opportunities About Us Search

DQS0: DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan

In the DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan, instructor Christopher Bradley (a CDMP Fellow) addresses the core Data Management discipline of Data Quality. It focuses on the most essential aspects of Data Quality Management and provides practical takeaways that will enable you to start a Data Quality initiative or program in your organization. It also helps prepare attendees to take the Certified Data Management Professional (CDMP) Data Quality Specialist exam, which is necessary to attain the Practitioner and Master levels.

Information and data are at the center of all organizations. However, all too often information is poorly managed and lacks the rigor and discipline that it demands. The implications of poorly managed information can be catastrophic, from ICO and other regulatory sanctions, to ineffective reporting, and ultimately to business collapse. This course provides the rationale for why Data Quality Management is critical and gives methods and practices for addressing key Information Management challenges within a Data Quality perspective.

The course draws upon real-world knowledge from successful Data Quality implementations and the lessons learned from them. Taught by instructor Christopher Bradley (a CDMP Fellow), this course offers a solid foundation in the topic of Data Quality Management, while also doing a deep dive into the activities, principles, and processes involved in developing an effective Data Quality function.

The training presents methods and practices for addressing key Data Quality challenges. It is grounded in the industry standard DAMA International Data Management Body of Knowledge (DMBoK).

The training also prepares attendees to take the Certified Data Management Professional (CDMP) Data Quality Specialist exam that is necessary for the attainment of the Practitioner and Master levels.

The DAMA International-endorsed live online training was developed with DAMA International, DATAVERSITY, and Christopher Bradley, the former VP of Professional Development at DAMA.

CDMP Specialist Exam Preparation Information:

Understand the in-depth information required for the CDMP Data Quality Specialist exam
Throughout the course, practice by taking sample questions in each section
There is a full, comprehensive practice test at the end of Course 5, each of the other courses have shorter review tests

Learning Plan Outcomes:

Get prepared for taking the Data Quality CDMP Specialist exam
Discuss categories of Data Quality issues from real-world case studies and their root causes
Explore the drivers for Data Quality and how to link Data Quality to business priorities
Understand the difference between “Data Quality” and “Data Quality Management” and why it matters
Learn about the relationship between Data Quality Management and other core Information Management disciplines – particularly Master Data Management, Data Modeling, and Data Governance
Discuss the necessary steps for making this happen through a practical framework
Explore who is involved in making Data Quality initiatives work
Understand the major concepts that are fundamental to Data Quality management, such as a framework for Information Quality, information lifecycle, Data Quality dimensions, business impact techniques, root cause analysis techniques, and more
Discuss where software tools and automation can play a part in a Data Quality initiative, and the key functional capabilities expected of Data Quality toolsets

Who Should Take This Learning Plan?

This learning plan is intended for business and IT professionals at all levels who have been charged with investigating or implementing Data Quality in their organizations and who seek to gain an overview of the different disciplines of Data and Information Management. It was also developed as a preparation course for people wishing to take the CDMP Data Quality Specialist exam, and much of the material focuses on the necessary skills to pass that exam.

It does assume general business knowledge, but not specific technical knowledge or experience. It is appropriate for executives, departmental and/or project managers, data and enterprise architects, consultants, data modelers, BI and data warehouse developers, data and business analysts, DBAs, technical staff, and anyone else interested and involved in data. The course is not designed for any specific business domain, and as such is applicable to any business function in need of more reliable information, such as finance, manufacturing, human resources, analytics, operations, and more.

Learning Plan Price: $799
Individual Course Price: $199
Learning Plan CEUs: 10.0 hours

Each Course Includes:

A 73- to 121-minute educational training video
An 18- to 30- question content-based exam after each course
After Course 5 (the final course in the Learning Plan), there is a 30-question CDMP-style practice exam
The other final exams and quizzes for each course are content-based practice tests
“Check for Understanding” quizzes after each course section
Presentation slides to assist in studying for the CDMP
Self-paced and on-demand e-learning
Unlimited course access

Courses within the DMBoK and CDMP – Data Quality Specialist Learning Plan:

Data Quality Overview, Definitions, Business Drivers, and Considerations
Data Quality Activities, Approaches, Requirements, and Governance
Data Quality Dimensions and Measures
Data Quality Rules, Cleansing, Matching, and Fit for Purpose
Data Quality Statistics, Tools, and Analysis Techniques

We offer several bulk licensing options for corporate and group use.

Course 1: Data Quality Overview, Definitions, Business Drivers, and Considerations

DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Course Overview
Course Curriculum
Course Outline (Course 1)
Overview, Definitions, and Exam Coverage
- CDMP Overview
- CDMP – Career Path
  - Associate
  - Practitioner
  - Master
- Question Bank
- Data Quality CDMP Module Breakdown
- Data Management Disciplines (DMBoK 2)
- Data, Information, and Knowledge (Information Value Chain)
- Business Environmental Factors
- Data Quality Management
- Key Points
- Data Errors Over Time
- Root Cause Analysis (Definitions / Basics)
- Why Data Management is Critical
- The Problem
Drivers, Exercises, and the DQM Cycle
- Business Drivers
- Direct Costs Associated with Poor Data Quality
- Benefit and Impact
- Exercise #1: Data Quality Problems
- Data Quality Management Cycle
- Key Definitions
- Drivers for Data Management
- What is Data Quality Management? Exercise #2: Data Issues Customer Expectations
- The Impact of Poor Data Quality
  - Real-Life Examples
- How CEOs Recognize Data as a Corporate Asset
- What Can and Can’t be Achieved?
- Data Quality Programs – Guided by Principles
- Data Quality Basics
Course 1 - Quiz #1
- Course 1 – Quiz #1 Answers
DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Progress Review

Course 2: Data Quality Activities, Approaches, Requirements, and Governance

DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Course Overview
Course Curriculum
Course Outline (Course 2)
Question Bank
Data Quality CDMP Model Breakdown
Data Governance
- Data Quality Management
- Data Quality and Data Governance: Their Interdependency
- How CEOs Recognize Data as a Corporate Asset
- What Can and Can’t be Achieved?
- What is Data Quality Management?
- Data Quality and Governance
- Containments of a Data Quality and Governance Strategy
- Data Quality (DQ) / Data Governance (DG) Framework: Vision and Strategy – Key Questions
  - DQ/DG Framework: Organization and People – Key Questions
  - DQ/DG Framework: Processes and Workflows - Key Questions
- A Simple Data Quality Improvement Framework
  - Data Quality Improvement Framework
Data Quality Activities
- Data Quality Management Cycle
- The Data Quality Management Approach
- Juran Trilogy
- Operationalizing Data Quality
Data Quality Readiness
- DQ Approaches
- Example Maturity Assessment
- DQ Readiness and Maturity
  - 1) Develop and Promote Data Quality Awareness
    - Value and Impact of Data Quality
      - Business Impact
  - 2) Define Data Quality Requirements
    - Data Model Assertions
      - Data Model and Data Quality
    - Information Lifecycle Vs. SDLC
      - The Information Lifecycle
    - Measuring Data Quality
  - 3) Profile, Analyze, and Assess Data Quality
    - Data Quality Profiling – Assess Data Quality
    - Typical Outputs of Data Quality Profiling
      - Data Profiling
- Column Level Stats 1: Summary Info
  - Validation Rules
  - Rules-Based Monitoring: Validation Rules
Exercise #1: Advantages of Data Quality Profiling
- Five Advantages of Data Quality Profiling
Course 2 - Quiz #1
- Course 2 - Quiz #1 Answers
DMBoK And CDMP Preparation: Data Quality Specialist Learning Plan – Progress Review

Course 3: Dimensions and Measures

DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Course Overview
Course Outline (Course 3)
Course Curriculum
Question Bank
Data Quality CDMP Module Breakdown
Dimensions of Data Quality
- Data Quality Dimensions (DMBoK2)
- DMBoK Dimensions of Data Quality
- Caution with Validity
- The Path to Accuracy
- Applying the Dimensions of Data Quality
  - Six Dimensions of Data Quality
  - Dimensions of Data Quality:
    - Completeness
    - Uniqueness
    - Timeliness
    - Validity
    - Accuracy
    - Consistency
- Other Data Quality Considerations
  - Standards (In Addition to the Dimensions)
Course 3 - Quiz #1
- Course 3 - Quiz #1 Answers
Data Quality Measures
- Data Governance and Data Quality - Linked
- Data Quality Measurement
- Impact and Cost Vs. Difficulty
- Data Quality Measurement Hierarchy
- Data Quality Indicators (Metrics)
  - Define Data Quality Indicators
    - Caution with Defining Data Quality Indicators
  - Example Data Quality Indicators
    - Perspective Awareness
Course 3 – Quiz #2
- Course 3 – Quiz #2 Answers
DMBoK And CDMP Preparation: Data Quality Specialist Learning Plan – Progress Review

Course 4: Data Quality Rules, Cleansing, Matching, and Fit for Purpose

DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Course Overview
Course Curriculum
Course Outline (Course 4)
Data Quality Rules
- Attribute Properties and Domains
- Attribute Domains
- Define Data Quality Business Rules
- Common Business Rule Types
- Data Quality Rules are Metadata
- Importance of Business Rules in Data Quality (DQ)
- Test and Validating Data Quality Requirements
- Set and Evaluate Data Quality Service Levels
- Continuously Measure and Monitor Data Quality
  - Example (UK NHS)
Exercise #1: Data Quality Reporting and Feedback Mechanisms
- Matching Situation to the Most Appropriate Feedback Mechanism
- Managing Data Quality Issues
Data Cleansing
- Demystified
- Data Enrichment (Examples)
- Clean and Correct Data Quality Defects
- Data Correction
Data Matching
- Master Data Match Rules
- Master Data Matching
- Example: Metadata Harvesting and Discovery
- Exact String-Matching Algorithms
- Approximate String-Matching Algorithms
- Uses of String-Matching Algorithms
- Monitor Operational DQM Procedures and Performance
- Data Governance Office
Course 4 – Quiz #1
- Course 4 – Quiz #1 Answers
DMBoK And CDMP Preparation: Data Quality Specialist Learning Plan – Progress Review

Course 5: Data Quality Statistics, Tools, And Analysis Techniques

DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Course Overview
Course Curriculum
Course Outline (Course 5)
Statistics and Data Quality
- Statistics 101
  - Mean
  - Median
  - Mode
  - Range
- Standard Deviation and Variance Defined
  - Variance Formula
  - Standard Deviation Formula
- Bell-Shaped Curve
Course 5 – Quiz #1
- Course 5 – Quiz #1 Answers
Data Quality Tools and Techniques
- Parsing
- Data Quality Firewall
- Data Quality Profiling – Assess Data Quality
  - Typical Outputs of Data Quality Profiling
Root Cause Analysis
- Remediation of Problems – Root Cause Analysis (RCA)
  - RCA Defined
  - Three Basic Types of Causes
  - RCA Process
- Failure Mode and Effects Analysis (FMEA)
- Five Why’s
  - Example #1
  - Example #2
- Preventative Actions
Data Quality Summary
- Traps for the Unwary – Why Data Quality and Data Governance can Fail
- Common DQ Mistakes
- Summary: Data Quality Management
- Links to Additional Content
  - Data Quality Resources
Course 5 – Quiz #2
- Course 5 – Quiz #2 Answers
DMBoK and CDMP Preparation: Data Quality Specialist Learning Plan – Progress Review