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course | Data Quality Management

Data Quality Management course designed for professionals in Abu Dhabi. Learn, apply, and get certified in data quality.

DIGTR-760 | Data Quality Management

Course Sector : Digital Transformation and Innovation

Course dates

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Duration	Date from	Date to	Course Venue	Course fees	Book a course
5 Days	21/09/2026	25/09/2026	Abu Dhabi	$4,250	Book now
5 Days	28/12/2026	01/01/2027	Dubai	$4,250	Book now
5 Days	24/01/2027	28/01/2027	Riyadh	$4,250	Book now
5 Days	26/04/2027	30/04/2027	Barcelona	$4,950	Book now

Course Introduction

This 5-day Data Quality Management training program addresses the key aspects of Data Quality management & provides practical take-away actions that will enable you to start a Data Quality initiative in your organization.

The training program draws up the Data Quality discipline as defined in the DAMA body of knowledge (DMBoK).

Course objective

Understand The Business Rules of Data Quality.
Identify Data Lifecycle Management (DLM).
Understand Data Governance Management Standards and Compliance.
Identify the Differences between Good data quality and Bad data quality.
Understand Data Quality Management approach and Scenarios.
Identify DQ Tools and features.
Understand the DAMA DMBoK Wheel.
Master using data analysis Techniques.
Master use profiling to assess Data Quality.
Identify Data Correction Techniques.
Understand How to Demystify Data Cleansing.
Understand DMBoK Dimensions of Data Quality.
Master applying Data Quality Dimensions.
Understand how to apply the Root cause analysis process and the 5-Why Approach for better DQ.
Define the financial cost of data quality management.
Understand How to Create the Data Quality Roadmap.
Master avoiding common data quality mistakes.
Create a Data Governance Management Action Plan.

Course audience

Data Quality Manager
Data Analyst
Data Governance Officer
Data Scientist
Business Intelligence Analyst
Data Engineer
Chief Data Officer (CDO)
IT Compliance Manager
Master Data Management (MDM) Specialist
Risk and Compliance Officer

Course Outline | Day 01

Data Measuring and Profiling Techniques:

The importance of measuring Data Quality.
Data analysis Techniques.
How can profiling assess Data Quality?
Typical Outputs of Data Quality Profiling.

Course Outline | Day 02

Validation Rules of Based Monitoring.

Data Correction Techniques.
How to Demystify Data Cleansing?
Data Modelling.
Data Flow Diagrams (DFDs).

Course Outline | Day 03

Applying DMBoK Dimensions of Data Quality:

Data Quality Facets (DMBoK).
What are the Dimensions of Data Quality?
Understanding DMBoK Dimensions of Data Quality.
How to apply Data Quality Dimensions?

Course Outline | Day 04

Data Quality Indicators (DQI) Characteristics.

What is the link between Governance and Data Quality?
Understanding Root cause analysis process and the 5-Why Approach: how to apply them for better DQ?
Defining Financial Cost.

Course Outline | Day 05

How to Create the Data Quality Roadmap

How to avoid common data quality mistakes?
Create a Data Governance Management Action Plan.
Workshop.

Course Certificates

BOOST’s Professional Attendance Certificate “BPAC”

BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.

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Data Quality Management (DQM) is the process of ensuring that data is accurate, complete, consistent, timely, valid, and reliable throughout its lifecycle. It involves establishing policies, standards, monitoring processes, and governance practices to maintain high-quality data for reporting, analytics, and decision-making.

While frameworks vary, five widely accepted principles of data quality are:Accuracy – Data correctly represents real-world values.Completeness – All required data is present.Consistency – Data is uniform across systems and datasets.Timeliness – Data is current and available when needed.Validity – Data conforms to defined formats, rules, and business requirements.

The four key stages of quality management are:Quality Planning – Define quality standards, objectives, and processes.Quality Assurance (QA) – Establish processes to prevent quality issues.Quality Control (QC) – Monitor and test outputs to identify defects.Quality Improvement – Continuously analyze performance and implement improvements

There is no universally recognized "5 C's of data quality" framework, but a commonly used model includes:Correct – Data is accurate and error-free.Complete – All necessary information is included.Consistent – Data is standardized across sources.Current – Data is up to date and relevant.Compliant – Data complies with governance, privacy, and regulatory requirements

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Since 2001, we’ve been at the forefront of professional training in the Middle East — shaping the future of learning and development one success story at a time. With a vision rooted in innovation and excellence, we help individuals, teams, and organizations reach their highest potential through integrated, future-ready training solutions. Our comprehensive programs combine global best practices with local insights, empowering people to grow, lead, and make a lasting impact in their industries.

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