Data Warehouse Training

How can you transform data into information, and then into knowledge, to create a learning organization? What layers of transformation are involved? In this first module, we explore the conceptual framework and the importance of a well-designed data warehouse. We examine the primary goals of a data warehouse and how it can improve data quality, information recognition, and retrieval. Several best practices are presented, including one from Ahold. We also discuss alternatives to a traditional data warehouse, such as appliances, in-memory BI, and data virtualization, along with relevant legislation on personal data, such as the GDPR.

To build a successful data warehouse, you need the right tools – tools that align with the overall business architecture. This alignment is essential for achieving long-term success.

What is the importance of a solid data warehouse architecture, and what does it look like in detail? How does a data warehouse align with your organization’s enterprise architecture? The instructor will introduce the different “schools” of data modeling, including those of Bill Inmon, Ralph Kimball, and Dan Lindstedt (Data Vault). What are their key differences and similarities, and what are the pros and cons of each methodology? The instructor will guide you through these topics and provide hands-on practice with various challenges and tasks.

When should you choose a particular philosophy – or combine elements from multiple schools and models? How should you incorporate Big Data into your data warehouse architecture? This kind of data often comes in large volumes or unstructured formats, such as emails, reviews, photos, videos, and voice recordings, which don’t naturally fit into standard data warehouse tables. Finally, the instructor will present the main database and modeling tools available and explain how to choose the right ones for your needs.

ETL tools – or data warehouse automation tools – are essential components of a data warehouse architecture. These tools allow you to model and automate the extraction, transformation, and loading of data. They also help streamline the automation process, accelerate development, and enhance data quality. In short, ETL is a must-have.

We will also cover various applicable data modeling techniques and explore how to best design the different process steps: extraction, transformation, and loading. In addition, the instructor will demonstrate the available ETL and data warehouse automation tools. When should you choose a single vendor, and when is a best-of-breed approach more suitable?

You’ll also be introduced to our 100% independent ETL & Data Integration Guide. Finally, the instructor will explain the difference between ETL and ELT, and we’ll examine the impact of increasingly decentralized data sources – both within and outside the organization – such as Big Data and data lakes.

How does a data warehouse provide high-quality data to end users, and what BI tools are available to support this? You’ll be introduced to our innovative Business Intelligence & Analytics Guide. The instructor will also highlight the latest trends in data warehousing and BI, with a special focus on Big Data, AI, and Data Science.

We’ll also explore the possibilities of open-source data warehousing and examine when direct access to the data warehouse is useful – and when it’s not. What types of users can you identify, and what are their specific functional needs? How can you best address those needs? And what role does self-service BI play in this context?

Having high-quality master data and metadata is crucial for delivering reliable and trustworthy information. Key data domains – such as customers, products, and employees – must be carefully maintained. Understanding their origin and how they are used, along with the related business rules, is essential.

But what exactly do we mean by master data and metadata? How do they contribute to delivering accurate, consistent information, and how do they support the goal of achieving one version of the truth? What processes are involved? Is master and metadata management solely analytical, or should it also influence operational systems? The instructor will explain how MDM and metadata management fit within the broader data warehouse architecture and how to establish a smart learning loop using an enterprise portal. After all, you want to apply information and insights in operational processes as quickly as possible.

Data warehouses require dedicated technical and functional management processes. In some cases, a Competency Center plays a central role. A suitable project approach and a clear understanding of success and risk factors are essential. What are the key success factors for a data warehouse? How important are maintenance and management? How do you develop a data warehouse with long-term support in mind? Which technical and functional management processes can be identified?

We’ll also review the most effective and modern tools available for this purpose, as well as the specific competencies and skills required for successful data warehouse management. How does this type of management differ from traditional maintenance, such as application management? What roles do business stakeholders and the IT department play? And what’s the true value – or lack thereof – of Competency Centers in this context?

Together, these topics will give you a complete picture and help you understand all the critical interdependencies.

This module covers all key aspects of Data Governance. Think of roles like data stewards, data custodians, and Chief Data Officers. But also consider data integrity, data quality, and accessibility of data and metadata. You’ll learn how to apply a Data Governance framework within your organization to move from basic governance toward excellence.

You’ll be introduced to the ideal path for achieving data perfection, along with the related costs and benefits. What steps should you take, and how can you elevate your organization to a higher level of data maturity?

The importance of high data quality should never be underestimated. But where do you begin, and what does it involve? In this module, you’ll be introduced to all the essential topics needed to significantly improve data quality. Think of aspects like completeness, correctness, integrity, interpretation, cleansing, metadata, and more. What should you focus on in your specific situation?

Additionally, monitoring and improving the quality of unstructured (sensor) data presents a major challenge. What are the best practices in this area, and how can data profiling support the process? What tools are available on the market to monitor and improve data quality? What are the key success and failure factors? And what can you learn from your own experiences?

In this section, you’ll learn about the continuous improvement process for the quality of (big) data, along with strategies to enhance overall data management. What steps can you take to secure progress and avoid recurring challenges?

This module offers in-depth insights into improving data quality using tools like the PDCA improvement cycle. How do you raise awareness? How do you engage employees and communicate the importance of data quality? How can you show them the value it brings? How do you address employee behavior and ensure buy-in? You’ll learn how to complete the cycle repeatedly (daily, weekly) and secure lasting results.