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If you are expecting … Power BI Data Modeling This session explains why data modeling is so important even if Power BI utilizes the in-memory columnar.

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Presentation on theme: "If you are expecting … Power BI Data Modeling This session explains why data modeling is so important even if Power BI utilizes the in-memory columnar."— Presentation transcript:

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2 If you are expecting … Power BI Data Modeling This session explains why data modeling is so important even if Power BI utilizes the in-memory columnar xVelocity engine or simply SSAS Tabular. Different data modeling approaches are compared from a performance point of view, but also the complexity of DAX measures is compared that are incorporated in different data models. New data modeling features like many-to-many relationships and aggregations have been released during the last months. This session demonstrates how these features can be used to solve complex problems and how these techniques can be used on top of large amounts of data. … you found the correct room

3 Power BI – Data Modeling
Tom Martens | SQL Saturday 810 Linz

4 We deliver Innovative Technology Consulting for Business
Program & Change Management 700 Professionals 6,000 Professionals 20 Länder 650 M€ Umsatz (gepl.) 2018 Technology Consulting 4,500 professionals Integration von Cloud Solutions 800 Professionals

5 Company Me tmartens@alegri.eu @tommartens68 Principal Consultant BI
> 20 Jahre BI, DHW und Analytics Data Platform MVP | Power BI Super User Founded in 2001 6.500 employees at the Devoteam-group, more than 250 employees at Alegri Offices at 11 locations 80% of DAX 100-Companies belong to our clients We partner with Microsoft and SAP Consulting Excellence in Microsoft Enterprise Solutions and SAP/ERP Solutions Neutral and Independent 100% participation of the Devoteam-group (July 2018) Basel @tommartens68

6 A Data Model … What t f !?

7 The one table solution …
good It’s fast whenever you are starting to explore a new dataset. It’s easy, at least until you start writing measures

8 Demo time

9 The one table solution … the good, the bad smart, the ugly deep
It’s fast whenever you are starting to explore a new dataset. It’s easy, at least Basically there is no smart way to pursue the one- table approach It’s not possible to convey all the information of your business objects into one table

10 Slide Objective: Showcase the business impact of adopting a data culture.
Building a data culture has a tangible impact on the bottom line. Here are some numbers that help bring some perspective to that transformation: According to IDC, organizations that are able to analyze all relevant data and deliver actionable insight will achieve an extra $430 billion in combined productivity benefits by 2020. According to Keystone, organizations evolving how they leverage data, analytics, and the cloud generate an average of an extra $100 million (or 8 percentage points) operating income per year. These improvements are a result of using analytical insights to transform business processes in areas like customer service or operations, for example. Let’s take a look at some companies that have been successful at implementing a data culture, and the role that Modern BI has played in their transformation.

11 The star schema still rules

12 Demo time

13 Scale Up and beyond maybe

14 The Power of aggregations
For your business Provide your users with the possibility to gain insights of large amounts of data For your developers You can use existing technologies, like SQL Server columnstore index as “pre-aggregate” For yourself Consider that maybe you have to spend some money, but also that it could be money well spend: The value of data

15 Demo time

16 Aggregations – some things to know
Build You have to build your aggregation table, this is not done by Power BI automatically Refresh You have to align the refresh of the aggregated data with the speed of change of the detail data Consider Currently there are these limitations in a composite model (that’s what’s necessary for aggregations), currently there are just 2 layer, detail and aggregate. The aggregations is determined on a table level and not on a column level.


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