3-day Data Warehouse & Data Governance training
Our data warehouse and data governance training will help you find the answers to important data questions. It will provide both theoretical and practical frameworks for designing a high-quality data infrastructure. There are many challenges ahead, such as:
- A data warehouse helps work towards one version of the truth, but will everyone accept this truth?
- You’re looking forward to the moment when management information isn’t delivered through an array of spreadsheets anymore, but how do you get rid of them?
- How can a data warehouse help you gain access to genuinely useful historical information, and what should you consider?
- How do you design your data warehouse and achieve very fast response times?
- Which ETL tools and data warehouse automation tools are available, and which ones are best in your situation?
- How do you “sell” a data warehouse to management and directors?
- What do you have to do to ensure that personal data is stored securely? How do you comply with GDPR (or similar regulations), and how do you design the data warehouse process around that?
- How do you set up a robust Data Governance structure and reach a certain degree of data perfection?
- How can you secure data quality?
- Finally: how to handle developments around Big Data such as data lakes and Hadoop?
If your organization faces one or more of these issues, our data warehouse & data governance training course is highly recommended.
Success and improvement with data warehousing
All the required elements for a successful data warehouse and its continuous improvement will be discussed over three intensive days. Those who follow this course will be well-equipped to successfully undertake a data warehouse or data governance project. You’ll want to to strive for a certain degree of data perfection in this, and of course you have to be mindful of the costs and benefits. But perhaps more importantly: you’ll be able to oversee the entire process and be the sparring partner of the business, (enterprise) architects, and all the way up to the technical people like database administrators; those who maintain the DWH environment.
Training in practical Data warehouse tools
Data warehousing and especially Data Governance are specialist fields. Working on the intelligence of an organization without having the right skills and tools frequently delivers counter-productive results. Our practical model, which covers all the data governance tools and methods will plot a clear route to real results. Even if there are ongoing big data projects in various places of your organization.
Discover the success factors behind data-driven organizations
Our Data Warehouse & Data Governance masterclass covers both the functional and technical aspects of (big) data. Consider the data warehouse, the architecture, master data management (MDM), metadata, data quality, data modeling, Data Governance frameworks, and maintenance. This all has to comply with rules and (security) regulations. The success factors of data-driven organizations will be especially closely examined.
Contents of the data warehouse & data governance training course
During this complete Data Warehouse & Data Governance course, you’ll be introduced to data warehousing, ETL, data warehouse automation, data quality, data governance, performance, Big Data, and developments in the Data Science field. We’ll also take a look at Business Intelligence as a higher goal of data warehousing, and the success (and failure) factors. Over these three days, we’ll help you become a sparring partner of everyone involved in this subject matter.
Day 1: Introduction, goals, alternatives & architecture
Introduction to data warehousing & management information
A data warehouse supports the concept of one version of the truth. This can be achieved by integrating and cleaning up data from various sources (business processes). That allows you to easily connect and analyze data.
- How can you transform data into information and information into insight? What developments are required? What is the importance of solid information maintenance?
- Which goals, such as history-building or performance improvement, does a data warehouse serve? How can a data warehouse contribute to improved data quality, recognizability, and findability? What does a data warehouse architecture look like in detail?
- Which DWH alternatives, such as appliances, in-memory BI, data lakes, and data virtualizations are useful and viable? What can we learn from practical case studies? What are your own experiences and what can you learn from them?
- What should you be aware of when it comes to GDPR? What can you do to still link data while complying with GDPR?
Data warehouse architecture
You need good tools in order to build a data warehouse. These should fit within the overall business architecture. Underlying consistency is essential for success.
- What is the importance of a solid data warehouse architecture? What does it look like in detail? How does a data warehouse fit within the organization’s enterprise architecture?
- What schools of data modeling are there? For example Bill Inmon, Ralph Kimball, and Dan Lindstedt (DataVault). What are the primary differences and similarities? What are the pros and cons of the various methods? When should you choose for one of them or a combination?
- How should you handle Big Data in your data warehouse architecture? This data is very large and volume and unstructured, containing things like emails, reviews, pictures, videos, and speech. This kind of data doesn’t fit within data warehouse tables.
- Which database and modeling tools are available, and how do you make the right choice? Which selection criteria should you be aware of?
The data warehouse & the ETL processes
ETL tools or data warehouse automation tools form an essential component in the architecture of a data warehouse. These tools enable you model and automate the extraction, transformation, and loading of data. They also contribute to the improvement of the computerization process, accelerate the development process, and improve data quality.
- Which data modeling techniques are applicable? How should you design the various process steps, like extraction, transformation, and loading?
- Which ETL tools and data warehouse automation tools are available? When should you choose one vendor, and when should you go for best-of-breed? We’ll introduce you to our 100% independent ETL Tools & Data Integration Survey.
- Which methods of improving quality, such as in the source or in the data warehouse, are there? What is data profiling and how can it help?
- What’s the difference between ETL and ELT? What is the impact of increasingly decentralized data collection such as Big Data and data lakes both within and without the organization?
Day 2: Analytics, Master Data Management, metadata & management
Business Intelligence & Analytics
The highest goal of the data warehouse is to deliver the right information to end users quickly. They can then use BI tools to request data, visualize it, and shape it into actionable insights.
- How does a data warehouse deliver high-quality data to end users? Which BI tools are available? Learn about our 100% vendor-independent BI Tools Survey.
- What are the latest trends in data warehousing and BI, especially concerning Big Data and Data Science? What does open source mean in this arena?
- When is direct access to the data warehouse useful and advisable, and when isn’t it? What types of users do you have, and what are their functional needs? How can you capitalize on this? What role does self-service BI play?
Master Data Management (MDM) & metadata
Having high-quality master and metadata is extremely important when delivering reliable information. Important data groups like customers, products, and employees have to be well-maintained. Insight into their history and edits, with accompanying business rules, is essential.
- What do we mean by master and metadata? How does it help with delivering high-quality, reliable information, and does it ensure one version of the truth? Which processes play a role in that?
- Is master and metadata management purely analytical or can it also impact operational systems? How do MDM and metadata management fit into the data warehouse architecture? How can you design a smart learning loop with an enterprise portal?
Data warehouse management & success
Data warehouses demand their own technical and functional management processes. Sometimes a Competency Center plays a dominant role in that. It’s important to take the right project approach and keep an eye on success and risk factors.
- What are the most important success factors for a data warehouse? How important is maintenance and management? How do you develop a data warehouse with support and maintenance in mind?
- What technical and functional management processes can you think of? Which tools are available for this?
- Do you need special competencies and skills for successful data warehouse management? How is DWH maintenance different from traditional maintenance, such as application management?
- What roles do business and IT play? How useful are Competency Centers in these roles?
Day 3: Data Governance, data quality, and continuous improvement
Organizations that want to work data-driven need a professional Data Governance structure. The entire data life cycle, from inception to deletion, has to be carefully shaped through processes. How do you establish a robust Data Governance structure, and what do you need to do so? How can you extract value from this and use it to achieve an advantage?
- The Data Governance framework: all aspects surrounding Data Governance are discussed. Consider the various roles, such as data stewards and data custodians. Also consider data integrity, data quality, and making data and metadata accessible. Learn to use a Data Governance Framework for your own organization.
- From Data Governance to data perfection: learn about the ideal way to reach data perfection in relation to the associated costs and benefits. Which steps should you take to lift your organization to a higher plane of maturity?
The importance of high-quality data should never be underestimated. But how can you safeguard the data quality, and what do you need to do so? This course will cover every topic related to ensuring high-quality data.
- Data quality: data can’t be half-right, it’s either right or wrong.
- What are the most important topics concerning data quality: completeness, correctness, integrity, metadata, etc. What should you focus on in your situation?
- What are the best practices concerning improving data quality of unstructured (sensor) data?
- Which tools can you use to monitor and improve data quality?
- Which success and fail factors play a role? What are your own experiences, and what can be learned from them?
Continuously improving data
During this segment you’ll learn more about the continuous improvement process of the quality of (big) data. Which steps should you take to secure results so that you don’t end up with the same data mess again and again?
- Gain insight into how to improve data quality using PDCA improvement cycles, among other ways.
- How do you create awareness? How do you convince employees of the importance of data quality, and what can it offer them?
- How do you deal with employees’ behavior and create a wide base of support?
- Learn to complete the cycle every time (daily, weekly) and secure the results.
Before diving deep into tools or data models, this three-day masterclass will provide a valuable overview of the playing field. And it will hand you all the ingredients required to achieve success with data warehousing and data governance.
There are group discussions and practical assignments during every day of the course, creating an ideal combination of theory and practice. After completing this course, you’ll receive a Passionned Academy certificate and a copy of Data Science for Decision-Makers signed by the author, Daan van Beek.
“Excellent. The Passionned Group provided me with an understanding & best practices and approaches that help me feel that a BI strategy + a DWH are within reach.”
This masterclass is meant for people who have to build a (big) data warehouse and maintain it, and those who have to deal with Data Governance issues. Participants of this course include: starting technical and functional project leaders, information managers, CRM managers, (chief) Data Officers, data warehouse managers, data analysts, management information employees, and (up-and-coming) BI & DWH managers.
Contact us now for in-company training
Our teachers would love to visit your company to share their knowledge. Our training courses can be tailored to your specific needs. Contact us to discuss all the opportunities.