Are you also struggling with the following questions?
Setting up a data warehouse and everything associated with it is not easy because:
- You know that with a data warehouse (DWH) you are working on “one version of the truth,” but can you enforce it?
- You eagerly await the moment when management information is no longer “conjured up” from a jumble of spreadsheets, but what do you do in the meantime?
- You want to have really useful historical information at your disposal. How can a data warehouse help you do that, and what should you think about?
- In what form should you cast your data model of a data warehouse and how do you achieve very fast response times?
- Which ETL tools and data warehouse automation tools are available and which is the best one for your specific situation?
- What is the best strategy if you want to put a DWH on the map with the business and management? In other words, ‘how do you sell the business case?’
- What should you pay attention to in order to guarantee the privacy of individuals? How will you comply with the General Data Protection Regulation (AVG) and how do you set up your data warehouse process accordingly?
- How do you set up a robust Data Governance structure and achieve a certain level of data perfection?
- How do you ensure that data quality within your organization gets and stays in order?
- And finally, how should you deal with developments around Big Data such as data lakes and Hadoop?
If you or your team are struggling with one or more of these questions, participation in our 3-day Data warehouse & Data Governance training course is highly recommended. Contact us and request a proposal for your company.
Build a tight data warehouse and set it up efficiently
You always strive for a certain degree of data perfection. Of course, you also have an eye for the costs and benefits of the project. But what is perhaps even more important: you are able to oversee the entire process and be a sparring partner for the business, (enterprise) architects as well as the technical people. Think for example of database administrators, ETL developers, data engineers and administrators. They have an important role in setting up and maintaining the DWH environment.
Training in practical Data warehouse tools
Data warehousing and especially data governance are specialized fields. Working on the intelligence of an organization without having the right skills and tools to do so often produces undesirable results. Our practical model covering all Data Governance tools and Data warehouse methodologies is the fastest and shortest route to resounding results. Even if there are, perhaps in different places in your organization, initiatives running around big data.
Contents of the Data warehouse & Data Governance course
During this complete Data warehouse & Data Governance course, in three days of three modules each, you will get extensively acquainted with data warehousing, ETL, data warehouse automation, data quality, data governance, performance, Big Data and developments in Data Science. But most of all, we will look at Business Intelligence as a higher goal of data warehousing and the success and failure factors. In these three days we are going to help you to be a full-fledged sparring partner of everyone involved in this matter.
Day 1: Introduction, goals, alternatives, ETL & data warehouse architecture
A data warehouse supports the principle of one version of the truth. You achieve this by cleaning up and integrating data from different sources (business processes). This allows you to easily relate and analyze the data.
- How can you transform data via information into knowledge? What transformation layers are involved? What is the importance of adequate information management?
- What goals, such as history building and performance improvement, does a data warehouse serve? How will the data warehouse contribute to improved data quality, recognizability and findability of information? What does a data warehouse architecture look like in detail?
- What DWH alternatives, such as appliances, in-memory BI, data lakes and data virtualization, make sense and are feasible?
- What can be learned from compelling real-world cases, such as that of Ahold? What are your own experiences and what can be learned from them?
- What issues should you be alert to when it comes to General Data Protection Regulation (AVG)? What methods are there so that you can still link and analyze data, but still comply with the AVG?
Data warehouse architecture
To build a data warehouse, you need good tools. These should fit within the overall enterprise architecture. The interrelationship is essential for ultimate success.
- What is the importance of a good data warehouse architecture? What does it look like in detail? How does a data warehouse fit within your organization’s enterprise architecture?
- What “schools of thought” for data modeling are there? Consider Bill Inmon, Ralph Kimball and Dan Lindstedt (Data Vault).
- What are the key differences and similarities? What are the advantages and disadvantages of the different methodologies?
- When do you choose which philosophy or put together a mix of the available models?
- How should you deal with Big Data in your data warehouse architecture? This data has very large volumes or is unstructured, such as emails, reviews, photos, videos and voice. After all, this type of data does not fit into your data warehouse tables.
- What database and modeling tools are there and how do you make the right choice in them? What selection criteria are important then?
The data warehouse and ETL processes
ETL tools or data warehouse automation tools are indispensable components in the architecture of a data warehouse. These tools allow you to model and automate the extraction, transformation and loading of data. They also help improve the computerization process, speed up the development process and improve your data quality. So ETL is a no-brainer. The concept answers the following questions:
- What data modeling techniques are applicable? How can I design the various process steps, such as extraction, transformation and loading?
- What ETL tools and data warehouse automation tools are available for this purpose? When do you go for one vendor and when do you choose best-of-breed? You will be introduced to our 100% independent ETL & Data Integration Guide.
- What are the methodologies for quality improvement, such as at source or in the DWH? What is data profiling and how can it help?
- What is the difference between ETL and ELT? What is the impact of increasingly large decentralized data collections such as Big Data and data lakes inside and outside the organization?
Day 2: BI & Analytics, master data management, metadata & management
A data warehouse is sometimes seen as a necessary evil, but it provides a good basis for steering information and policy evaluation. However, the data warehouse’s main function is to quickly deliver the right data at the request of your end users. These mostly use BI tools to query, visualize and transform data into actionable insights.
- How does a data warehouse deliver quality data to end users? What BI tools are available on the market? Also, get to know our innovative Business Intelligence & Analytics Guide.
- What are the latest trends in data warehousing and BI especially focusing on Big Data and Data Science? What is the significance of open source in this arena?
- When is direct access to the data warehouse advisable and useful, and when is it not? What types of users can you distinguish and what specific functional needs do they have? How can you anticipate these optimally? What role does self-service BI play in this?
Master Data Management (MDM) & Metadata
Having good master and metadata is extremely important in providing quality and reliable information. Important data groups such as customers, products and employees should always be maintained properly. Understanding the genesis and operations with associated business rules is essential to this.
- What should you actually mean by master and metadata? How does this data help deliver high-quality and reliable information and to what extent does it ensure one version of the truth? What processes are involved?
- Is master and metadata management purely analytical, or can and should it also impact operational systems? How do MDM and metadata management fit into the data warehouse architecture? How can you set up a smart learning loop with an enterprise portal?
Data warehouse management & success factors
Data warehouses require their own technical and functional management processes. Sometimes a Competency Center plays an important role in this. An appropriate project approach and an understanding of the success and risk factors are crucial in this regard.
- What are the key success factors for a data warehouse? How important is maintenance and management in it? How do you develop a data warehouse with support and maintenance in mind?
- Which technical and functional management processes can be distinguished? Which tools are available for this?
- Are special competencies and skills needed for successful DWH management? To what extent is DWH maintenance different from traditional maintenance such as application management?
- What role does the business play and what role does IT play? What is the sense and nonsense of Competency Centers in this regard?
Day 3: Data governance, frameworks, data quality and continuous improvement
Organizations that want to be data-driven can no longer do it without a professional Data Governance structure. You will need to shape the entire lifecycle of data – from creation to disposal – in a process-oriented way. How do you set up a robust Data Governance structure? What is involved? How can you derive value and benefit from it?
- The Data Governance framework: all aspects around Data Governance are covered. Think about different roles such as data stewards and data custodians. But also think about data integrity, data quality and making data and metadata accessible. You will learn to use a Data Governance framework for your own organization.
- From Data Governance to data perfection: you will be introduced to the ideal route to achieve data perfection in relation to the associated costs and benefits. What steps should you take and how do you lift your organization to a higher maturity level?
Never underestimate the importance of data quality
The importance of good data quality should never be underestimated. But how do you get started and what is involved? During this training you will be introduced to all the topics to achieve higher data quality.
- Data quality: data cannot be half right, data is either right or wrong.
- What are the most important data quality topics: completeness, correctness, integrity, metadata, et cetera? What exactly should you pay attention to in your situation?
- Improving the data quality of unstructured (sensor) data, what are the best practices?
- What tools are available on the market to monitor and improve data quality topics?
- What are the success and failure factors involved? What are your own experiences and what can be learned from them?
Continuous data improvement: make short work of ‘data clutter’
In this section you will learn more about the continuous improvement process of the quality of (big) data. Which steps should you take to secure results and not be confronted with the same ‘data mess’ over and over again?
- You will gain insight into the way you can improve data quality through the PDCA improvement cycle.
- How do you create awareness? How do you include employees and communicate the importance of data quality? How do you show what this can bring them?
- How do you deal with the behavior of employees and how can you ensure great support?
- You learn to complete the cycle every time (daily, weekly) and secure the results.
Before going in depth with tools or data models, for example, this valuable three-day in-company training gives you a clear overview of the playing field. And it provides you with all the ingredients to achieve success with data warehousing & data governance.
Discover the success factors behind data-driven organizations
Our three-day in-company Data warehouse & Data Governance course addresses both the functional and technical aspects of (big) data. These include the data warehouse, architecture, master data management (MDM), metadata, data quality, data modeling, Data Governance frameworks and management. All of this, of course, must also comply with all legal regulations and security guidelines. But above all, the success factors of a data-driven organization are discussed.
Interactive training: learn from other trainees
Every training day, group discussions take place and participants work on practical assignments. This creates an optimal mix between theory and practice. Upon completion of this unique data training course, participants will receive a certificate from the Passionned Academy and a copy of the ‘Data Science Book‘ signed by Daan van Beek.
Additional information of this Data warehouse training
This Data warehouse & Data Governance training is done in-company. Some of its features are listed below:
✪ exempt from VAT
✪ no study load
✪ authenticated digital certificate
✪ from 9:00 AM to 5:00 PM
This course is also offered in Dutch and it is part of our 10-day Data Science training and CBIP certification.
Target group of the Data Science course
The master class is designed for people who need to build or maintain a (big) data warehouse and those who have to deal with Data Governance issues. This Data warehousing course is often attended by: (starting) functional and technical project leaders, BI specialists and consultants, information managers, CRM managers, (chief) Data Officers, data warehouse managers, data analysts, BI & management information officers, data engineers, (upcoming) BI & DWH managers and anyone who wants to start making a difference with data.
Achieved learning objectives at the end of this DWH course
- You know the differences between BI and AI and how to deal with Big Data, data lakes and Hadoop
- You are able to carefully weigh the various alternatives for a DWH architecture
- You can assess which DWH architecture fits perfectly with your company’s enterprise architecture
- You can put AI & Data Science in context, as a welcome addition to Business Intelligence
- You are able to interpret DWH and Data Governance trends and translate them to your organization
- You understand the importance of data warehousing, ETL and data quality
- You can clearly explain the different theories for data modeling
- You have insight into the functionality of the most important ETL and data warehouse automation tools
- You can draw up a business case for a data warehouse (DWH)
- You know how to set up a DWH process and architecture
- You have learned how to set up a robust Data Governance structure
- You understand the principles of the General Data Protection Regulation (AVG)
- Thanks to interaction with fellow course participants, you will feel equipped to successfully manage a DWH project
Request more information
Through our contact form you can request more information or a proposal for our Data warehouse & Data Governance training. If you have any questions about this training, please contact us directly.
About the lecturer
The lecturer Daan van Beek Msc, is an authority in the field of Business Intelligence and AI. With over 20 years of experience with Data Analytics, Daan brings a wealth of practical insights to the table. His extensive background and experience around the world equips him to guide participants through the intricacies of data warehousing, data quality, and data governance. He designed this course many years ago and is continuously refining it.
Reviews about Data Warehouse Training
The content aligns well with the knowledge I currently require. I would appreciate the opportunity to discuss further the selected applications within our architecture (not limited to just MS Fabric).
Good explanation and clear! You notice that the trainer has a lot of experience and is able to adapt to each person. I am glad I took this course because I have many points I want to take with me to my company.
Lots of information, much of it was very useful. Lots of variety between practice and theory. And Dick (instructor) plays well into the questions from the group.
It is sufficiently interactive to ask questions specific to your own situation. Content goes very broad sometimes, so broad that it is difficult to apply in practice (very theoretical and conceptual).
Very positive, good basic course with interactive program.
It is very nice to hear the state-of-the-art from practitioners who support the theory with numerous practical examples. I myself also particularly enjoyed the interaction between the course participants during the execution of various practical assignments. Review of Training Datawarehouse & Data Governance