Training program Details ( Data Quality Fundamental )

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Data Quality Fundamental

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Data Quality Fundamental

Data is one of the basic pillars that organizations of all kinds depend on in all their internal operations, in addition to its importance in making strategic, tactical and operational decisions, which requires great attention to its quality in terms of availability, accuracy and completeness, which is called the quality of information, which is one of the branches of data science. This training program focuses on introducing the main features that express the quality of data, methodologies for ensuring the acquisition and maintenance of high quality information and best international practices to achieve this goal. In addition, the program reviews global standards for data quality (ISO 8000).

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Overview

Sectors

Banking

Capital Market

Insurance

Financing

Job Families

Topics

Artificial Intelligence and Data Management

Prerequisite Programs

Not Exist

Training Methods

Lecture

Case Studies +1

Evaluation

Pre Exam

Post Exam

Target categories

- +1

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Available Dates

This provides you with the opportunity to select the available times that suit you best for participation in our program. These times represent slots during which we are ready to welcome you and provide assistance and guidance.

Program Agenda

Schedule

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Day 1

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Day 2

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Day 3

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Module 1: Basic Definitions

In Class Training-Online Training

Data quality definition from different perspectives

Data quality vs data governance

Data vs information

Data cleansing

Data standardization

Data analysis

Data visualization

Data Data warehousing

Data science

Big data and machine learning

Benefits of high quality data

Data profiling

Data dimensions

Critical Data element

Data Types

Reference data

Master data

Transactional data

Analytical data

Enterprise data

Module 2: Data Quality Introduction

In Class Training-Online Training

The role of data in business

Benefits of high quality data

The effect of poor data

Challenges of data quality management

Who is responsible for data quality?

Data quality analyst roles

Module 3: Data Quality Attributes and Dimensions

In Class Training-Online Training

Accuracy

Validity

Timelines

Completeness

Uniqueness

Consistency

Module 4: Data Quality Management Methodology and Process

In Class Training-Online Training

Define DQ requirements

Data profiling

Conduct DQ assessment

Define data quality rules (6 dimensions) and data thresholds

Assessment by enforcing rules on existing data set

Identify data quality issues and update issues log

Resolve DQ issues

Root cause analysis

Issue resolution

Review data policies and procedures (if necessary)

Monitor and control

Define and populate data quality scorecards

Monitor data quality

Data quality process and system development life cycle (SDLC)

Module 5: Data quality tools

In Class Training-Online Training

Data profiling

Data quality assessment

Data quality scorecards

Data issues resolution

Data cleansing

Data standardization

Data integration

Data export and load

Root cause analysis

Module 6: ISO 8000: The Global Standard for Data Quality and Enterprise Master Data

In Class Training-Online Training

Introduction

Scope

Terms, definitions and abbreviations

fundamental principles of data quality management

Data quality management process

The implementation process

Planning

Quality control

Quality assurance

Data quality improvement

The data-related support process

Data architecture management

Data transfer management

Data operations management

Data security management

The resource provision process

Data quality organization management

Human resource management

The relation between data quality and data governance

Implementation requirements

Module 7: Practical exercise

In Class Training-Online Training

Test all data quality dimension

Demonstrate end to end data quality process

Use customer information and/or medical data to apply data quality concepts

Integrated Paths

Information Systems Governance

Program Targets

Understand the main concepts of data quality to recognize its position among other data concepts in order to build the appropriate framework for dealing with data.

Recognize the attributes of data quality to ensure the taking proper decisions.

Study the methodology of maintaining high quality data to build the appropriate framework for data management.

Recognizing the global standards for data quality to ensure a consistent application for obtaining international accreditations.