Data Quality Advanced (NL)

283486

What is Data Quality Advanced

The importance of data is ever increasing; many organisations cannot function without it, and far-reaching decisions are made based on it.
It can be disastrous if decisions are made on the basis of incorrect or misinterpreted data.
But how do you ensure that data is of sufficient quality to support processes and decision-making? In other words: that the data is “fit for use”? And how do you implement the processes and tools required to monitor and improve data quality?
In this training, we will cover the various approaches to data quality management, and how these can be implemented.
We will also discuss prerequisites for implementation, and the types of tools available in the market.
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 management implementations.

Who should attend Data Quality Advanced

This training is intended for:

• Solution Architects and Designers of data-driven solutions (e.g., BI, advanced analytics, regulatory reporting)
• Business and Information Analysts in data-driven organizations
• Data Stewards
• BI Consultants

All of those with basic knowledge of the various forms of data quality.

Prerequisites

Knowledge of basic DQ concepts like DQ dimensions and DQ analysis types is assumed.
If you lack this knowledge, please attend the “Introduction Data Quality” training first.
If you’re not sure, please contact us, and we will get a trainer to contact you and discuss.

Objectives

At the end of this training, the participant understands the processes and tools relevant to data quality management, and how these should be implemented in an organization.
The training aims to clarify the architecture and business concepts around these topics. Attention is also paid to the relationship of data quality management with other aspects of data management, such as data governance and master data management.

283486

What is Data Quality Advanced

The importance of data is ever increasing; many organisations cannot function without it, and far-reaching decisions are made based on it.
It can be disastrous if decisions are made on the basis of incorrect or misinterpreted data.
But how do you ensure that data is of sufficient quality to support processes and decision-making? In other words: that the data is “fit for use”? And how do you implement the processes and tools required to monitor and improve data quality?
In this training, we will cover the various approaches to data quality management, and how these can be implemented.
We will also discuss prerequisites for implementation, and the types of tools available in the market.
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 management implementations.

Who should attend Data Quality Advanced

This training is intended for:

• Solution Architects and Designers of data-driven solutions (e.g., BI, advanced analytics, regulatory reporting)
• Business and Information Analysts in data-driven organizations
• Data Stewards
• BI Consultants

All of those with basic knowledge of the various forms of data quality.

Prerequisites

Knowledge of basic DQ concepts like DQ dimensions and DQ analysis types is assumed.
If you lack this knowledge, please attend the “Introduction Data Quality” training first.
If you’re not sure, please contact us, and we will get a trainer to contact you and discuss.

Objectives

At the end of this training, the participant understands the processes and tools relevant to data quality management, and how these should be implemented in an organization.
The training aims to clarify the architecture and business concepts around these topics. Attention is also paid to the relationship of data quality management with other aspects of data management, such as data governance and master data management.