Introduction Data Quality (NL)

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What is Introduction Data Quality

The importance of data is ever increasing; many organizations cannot function without it, and far-reaching decisions are made based on it.
It can be disastrous if decisions are made based on incorrect or misinterpreted data.
Which characteristics define whether data is of sufficient quality to support processes and decision-making? In other words: when is the data “fit for use”? How can these characteristics be measured?
In this training, we will cover the various aspects of data quality, the methods that can be used to measure it, and the ways to present these measurements.
We will also discuss the main benefits and costs related to managing data quality.
During the training, the application of what you learn is central and you are challenged to work with the theory in practical cases.
The training is given by people with an extensive background in DQ Management. They know how it’s applied in practice and can therefore share “real-life” examples of data quality issues and the ways these were resolved.

Data quality is not just about theory. The expertise of our trainers adds a practical dimension to the theoretical concepts, providing real-world insights and best practices. This training will equip you with the knowledge and skills to recognize and improve data quality in your organization.

Who should attend Introduction Data Quality

• Solution architects and designers of data-driven solutions (e.g., BI, advanced analytics, regulatory reporting)
• Business Analysts and Information Analysts: Professionals who rely on data for business decision-making.
• Data Stewards: Individuals responsible for data quality in an organization.
• Data Analysts: Professionals who work with data daily and need to ensure its quality.
• IT Managers: Individuals who oversee IT projects and need to understand the importance of data quality.
• Project Managers: Professionals who manage projects involving large amounts of data.

Prerequisites

A basic understanding of data management concepts is beneficial but not mandatory.

Objectives

At the end of the training, you will be able to:

• Understand the various forms of data quality and the business and IT perspectives on it.
• Recognize the impact of low data quality on business processes.
• Have a basic understanding of the methods available to measure and improve data quality.

102476

What is Introduction Data Quality

The importance of data is ever increasing; many organizations cannot function without it, and far-reaching decisions are made based on it.
It can be disastrous if decisions are made based on incorrect or misinterpreted data.
Which characteristics define whether data is of sufficient quality to support processes and decision-making? In other words: when is the data “fit for use”? How can these characteristics be measured?
In this training, we will cover the various aspects of data quality, the methods that can be used to measure it, and the ways to present these measurements.
We will also discuss the main benefits and costs related to managing data quality.
During the training, the application of what you learn is central and you are challenged to work with the theory in practical cases.
The training is given by people with an extensive background in DQ Management. They know how it’s applied in practice and can therefore share “real-life” examples of data quality issues and the ways these were resolved.

Data quality is not just about theory. The expertise of our trainers adds a practical dimension to the theoretical concepts, providing real-world insights and best practices. This training will equip you with the knowledge and skills to recognize and improve data quality in your organization.

Who should attend Introduction Data Quality

• Solution architects and designers of data-driven solutions (e.g., BI, advanced analytics, regulatory reporting)
• Business Analysts and Information Analysts: Professionals who rely on data for business decision-making.
• Data Stewards: Individuals responsible for data quality in an organization.
• Data Analysts: Professionals who work with data daily and need to ensure its quality.
• IT Managers: Individuals who oversee IT projects and need to understand the importance of data quality.
• Project Managers: Professionals who manage projects involving large amounts of data.

Prerequisites

A basic understanding of data management concepts is beneficial but not mandatory.

Objectives

At the end of the training, you will be able to:

• Understand the various forms of data quality and the business and IT perspectives on it.
• Recognize the impact of low data quality on business processes.
• Have a basic understanding of the methods available to measure and improve data quality.