DAMA DMBoK Data Quality Specialist [CDMP-DQ]

OVERVIEW

This 1-day DAMA DMBOK Data Management Specialist course addresses all required disciplines required for the Data Quality (DQ) specialist exam by the international standard, DAMA Body of Knowledge (DMBOK2) and is aimed at individuals interested in developing concrete professionalism in the field of Data Management with a specialization in Data Quality.

This course provides a solid foundation of the different information disciplines across the complete Data Quality spectrum.

The DQ specialist course explores the essential concepts and techniques for ensuring data accuracy, consistency, and completeness. This includes understanding data profiling methodologies to assess data quality issues and anomalies. You'll delve into strategies for data cleansing, deduplication, and standardization to improve data quality. By the end, you'll be equipped with the knowledge and skills to establish robust data quality processes and metrics within your organization.

OBJECTIVES

• Grasping the core concepts and principles of data quality, including accuracy, completeness, consistency, and timeliness.
• Learning how to conduct data profiling to assess the quality of datasets and identify anomalies, inconsistencies, and errors.
• Mastering various data cleansing techniques, such as deduplication, standardization, and validation, to improve data quality.
• Developing skills in establishing data quality processes, policies, and metrics to monitor and measure data quality over time.

AUDIENCE

The course of Data Quality Specialist is designed for Analytics Managers, IT Managers, Data Quality Professionals, Data Engineers, Data Analysts and Scientists, Database Administrators, Database Modelers, and professionals interested in Data Quality.

The following job roles also qualify:

• Business Intelligence & Data Warehouse developers & architects
• Data Modellers
• Developers
• Data & Enterprise Architects
• Data Analysts
• Solution Architects
• Application Architects
• Information Architects
• Business Analysts
• Database Administrators
• Project / Programme Managers
• IT Consultants
• Data Governance Managers
• Data Quality Managers
• Information Quality Practitioners

CERTIFICATION

Attending this course will give the learners a solid preparation to sustain the following specialist certification exam:

• DAMA Certified Data Quality (CDMP)

This exam is not included in the course price and can be booked in addition to the course.

Additional Certification Requirements:

• CDMP Practitioner: 70% pass in Data Management Fundamentals exam and 70% pass in 2 specialist exams and between 2 and 10 years of Industry Experience.
• CDMP Master: 80% pass in Data Management Fundamentals exam and 80% pass in 2 specialist exams and more than 10 years of Industry Experience.

CONTENT

Course Content

• Overview of data quality concepts, importance, and implications for organizations.
• Understanding the dimensions of data quality: accuracy, completeness, consistency, integrity, reasonability, timeliness, uniqueness, and validity.
• Techniques for conducting data profiling to assess the quality of datasets.
• Implementing data cleansing processes to improve data accuracy and consistency.
• Establishing data quality metrics and standards to measure and monitor data quality.
• Understanding the role of data governance in ensuring data quality and integrity.
• Tips and strategies for successfully completing DAMA certification exams in data quality.

Agenda of the Course

The course includes training, practice, and downloadable materials for the facilitation of learners. The main sections of the training are:

• Introduction
  Relations with other DAMA topics, drivers, goals, and principles.
• Essential concepts
  Quality, critical data, DQ dimensions, DQ improvement Lifecycle, business rules, common causes of DQ issues, data profiling, data cleansing, data parsing, and data transformation.
• Activities
  Define high quality data, define a DQ strategy, identify critical data and business rules, perform DQ assessment, identify potential improvements, develop and deploy DQ operations, measure and monitor DQ.
• Tools & Techniques
  Data Profiling, Query tools, Modelling and ETL tools, Metadata repositories, preventive and corrective actions, statistical process control, and root cause analysis.
• Implementation
  Readiness assessment, Risk assessment, and organizational change.
• Governance
  Roles, policies, and metrics.

Benefits

There are several benefits of participating in the Data Quality Specialist course. Some of them include:

• Learners get a solid foundation on data quality.
• Learners become certified data quality Specialists.
• Learners learn to understand the principal concepts and terminology.

This course is designed to facilitate the learning and understanding of learners about data quality and its related concepts.