Markus Ehrenmüller-Jensen is the founder of Savory Data and works as a project leader, data engineer and BI architect since 1994. He is an educated software-engineer, graduated business educator and professor for databases and project engineering at HTL Leonding (technical college) and certified as MCSE Data Platform and MCSE Business Intelligence. Markus speaks regularly on international conferences (eg. SQL PASS Summit, SQLBits London, Power Platform Worldtour, SQL Saturdays, SQL Days, SQL PASS Austria, ...) and writes articles for well-known journals. In 2013 he co-founded SQL PASS Austria and in 2016 Power Platform Usergroup Austria (both were merged into Data Community Austria in 2021) and organizes Data Community Austria (fka. SQL Saturdays & Data Saturdays) in Vienna since 2014. For his technical leadership in the community he was awarded repeatedly as a Microsoft Data Platform MVP since 2017.
Area of Expertise
Self-Service AI with Power BI
Power BI Desktop is Microsoft’s free tool for self-service BI. It’s updated every single month with exciting new features and Gartner declared it since 2019 to THE leading BI Tool (which led Tableau and Qlik behind). In this full day workshop, we take a look on Power BI Desktop’s possibilities concerning Artificial Intelligence. The functionalities are reaching from simple context menus, to e. g. get an explanation for a peak in a value over time, to ingesting a self-deployed Azure Machine Learning web service.
We will touch some of the following smart features:
• Q & A
• Smart Narrative
• Quick Insights
• What-if Parameter
• Analytic Line
• Anomaly Detection
• Data Profiling
• R & Python Integration
• Smart Custom Visuals
• Key Influencer Visual
• Column by Example
• Decision Tree
• Cognitive Services
• Azure Machine Learning
The demos show both, no-code solutions and complex scripts in DAX, R, Python and M. Knowledge in those languages are helpful but not necessary.
My Favorite Pie (Chart): Simple Rules for Clear Visualizations
Dataviz guru Stephen Few once stated that we should “save the pies for dessert”. What he meant is that pie charts are good in some specific use cases, but they should not be used in others. The same is true for other chart types. Using the wrong type of chart will make it harder for report users to understand the story behind the data. To enable insights, information has to be presented in the most intuitive way possible. You will leave this session with five easy-to-implement rules, which will guide you through the process of creating clear and attractive visualizations.
Model Your Data Like a Star in Power BI
Power BI is a self-service Business Intelligence tool, which does a great job in hiding complexity and technology away from the user. But when it comes to performance and even correct numbers, one should spend some time in finding the right way of modelling the tables and how they are related to each other. In this talk we spend quality time on doing exactly that. You will get answers to questions like: Why should you care about the data model? What kind of relations are available? How do they influence performance? Why is redundancy (not) a bad thing?
After this session you will understand how to model data like a star.
Key-value-pair Tables in Power BI, AS and/or Your DWH
Key-value-pair tables have basically only two columns: A key and a value for that key. Such tables offer great flexibility for an application, as new keys (= attributes) easily can be added, without the need to change the structure of the table. Any content can be stored in the value column, as it is of type text.
What is amazing for application developers, can turn out as a nightmare for report developers: Most Business Intelligence tool needs attributes in dedicated columns (with the right data type) to allow for aggregations. This means that the table has to be pivoted.
In this talk, Markus will show a solution he implemented for a client and successfully runs in production. He semi-automated pivoting and the assignment of the right data type and will show how to achieve this in either Power Query (self-service BI) or SQL database (enterprise BI).
From Report to Interactive Data Applications with Power BI
Reports were yesterday – data applications own the future! In this demo rich session you will learn how to leverage Power BI Desktop’s interactive features to transform your existing reports into highly interactive applications which allow the report user to discover the data in an easy way.
You will learn about: Bookmarks, Drilling, Drill-through, Tooltips, What-if parameters and more!
Do You Speak English? Localized Reports with Power BI
Even when we live in a global world your end-users might expect to get their reports in their own local language. This talk is guiding you through the available options and necessary steps to give the report user control over the language:
• Content of textual columns
• Model (names of tables, columns and measures)
• Power BI Desktop and Power BI service
You will learn to extend Power BI’s data model to allow for multi-language support of column content and headlines (and how you can automate the translation of the texts with Azure Cognitive Services). I will show you how you can implement currency conversion and how to translate the model’s meta data. Finally, we look at how to change the language in Power BI Desktop in in Power service.
Advanced Data Modelling in Power BI/AAS
While it is a good practice to model your data as a star schema, there are challenges where you have to reach out to more advanced concepts. In this 60 min we will discuss the following challenges and how to overcome them:
• Multi-Fact model
• Many-to-many relationships
• Detached table
• Key-value-pair model
5 Things You Need to Know When Learning DAX
When learning DAX you will soon discover, that it is both, “easy, but hard” (© Alberto Ferrari). On the one hand, the syntax of this language is very close to Excel formulas and easy to understand. On the other hand, the semantic of some functions is very challenging. In this talk Markus will share the challenges he faced when learning the language and how overcame them. You will get a proper understanding of the differences between calculated columns and measures and you will learn of the concept of context and how to manipulate the context to achieve advanced calculations.