LoQutus: A deep-dive into Microsoft Power BI Contact us at info@loqutus.com for the case and instruction files!

1. Supported by:
Connected Brains 2019
Power BI Deep-DiveWorkshop

2. Contact
• Thomas D’Hauwe
• Data Engineer / Data Architect
• thomas.dhauwe@loqutus.com
• Pieter-Jan Serlet
• Data Engineer
• pieter-jan.serlet@loqutus.com
• Tom De Roover
• Data Architect
• tom.deroover@loqutus.com
• SanderVan Driessche
• Data Analyst
• sander.vandriessche@loqutus.com
• Thomas Michem
• Lead Analytics & Insights
• thomas.michem@loqutus.com

3. Session
Promise
(hide)
A key tool in any Analytics toolset is a data visualisation tool.
While there are many good options, recently Microsoft Power
BI is taking a clear lead in the space.
But it can be hard to keep up! In this short session we will get
you up to speed with the basics and our pick of top features
that will make your data exploration efforts stand out.
From embedded machine learning with clustering, forecasting
and quick insights, to complex calculations and advanced
visualisations.
This session will be hands-on, you'll go through the key steps
and you'll walk out with a working dashboard and inspiration
to apply the lessons learned in your context.

4. Welcome! Our Goal
Today
• Deep Dive in Power BI
• Exploring a common case
• Highlighting top features
• Analytics FTW
• Take home examples!

5. CASE
• Building an advanced dashboard
• For SupportTicket Follow-Up
• Based on JIRA Service Desk
• Using advanced Power BI features

6. Data Model
TICKETS SLAs
Backlog
Evolution
Date

7. JIRA – Follow Up SLA’s
• Within JIRA Service Desk SLA’s are defined
• SLA’s that are not met are a ‘Breach’
• In the dashboard we want to follow up the following
• Time to first response – Do we respond to the ticket in time
• Time to workaround – Do we provide a workaround in time
• Time to resolution – Do we provide a final solution in time

8. AnalyticsValue Chain
WHAT HAPPENED?
Descriptive Analytics
WHY DID IT
HAPPEN?
Diagnostic Analytics
WHATWILL HAPPEN?
Predictive Analytics
HOW CANWE MAKE
IT HAPPEN?
Prescriptive Analytics
Data
Stories
Data
Stories
DashboardsDashboardsDashboards
ReportsReports
VisualsVisuals
Analytic
Models
Analytic
Models
Data
Driven
Apps
Data
Driven
Apps

10. Microsoft Self-Service Stack
SSAS TABULAR
05

11. Aren’t you tired of this?

12. DAX - Data Analysis Expression
Language
https://community.powerbi.com/t5/Data-Stories-Gallery/DAX-Reference-Cheat-Sheet/td-p/483212

13. Power Pivot

14. Power BI - The Content Pack Way
https://www.atlassian.com/blog/add-ons/jira-content-pack-for-microsoft-power-bi
https://powerbi.microsoft.com/nl-nl/blog/explore-your-jira-data-with-power-bi/

15. The Tailored Dashboard (LoQutus)Way

16. Advanced Power BI
• We’ll start from a template
with the basics already covered
• Deep Dive into the advanced
(but common) features
• Take home samples with
detailed instructions

17. Basic POWER BI CHECKLIST
‘Getting Started Is Easy’
 I can open Power BI desktop
 I can Get Data into Power BI
 I know how to edit my data
 I can do some Modelling (e.g. defining relations)
 I can create visualisations
 I can make them pretty
 I can publish my dashboard to power BI online
 I can get Quick Insights

18. Get Power BI BI DESKTOP
Download (free) Power BI DesktopStep 1 – Download
https://powerbi.microsoft.com/en-us/
Be prepared for
Monthly updates!
• Step 2 - Open

20. GET DATA

21. Clean data = Edit Queries

22. Correlation
Evolution
Comparison
Detail
KPIs
Geospatial
VISUALS

23. Components

24. Advanced POWER BI CHECKLIST
 I can add calculated Tables
 I can add complex DAX formula’s
(using variables)
 I can add forecasting to time series data
 I can add Marketplace Visuals
 I can set up a Drillthrough Page
 I can use Machine Learning for quick
insights

25. Data Model
TICKETS SLAs
Backlog
Evolution
Date

26. Prepped Data Model

27. Backlog Analysis - Forecasting

28. Backlog Analysis
• Issue: from the list of tickets it’s hard to get the
‘backlog’ – the number of open tickets for a specific
date
• To perform the backlog analysis in Power BI:
• Add a date table to the data model
• Add the necessary calculations for the backlog

29. CALCULATED COLUMNS MEASURES
DAX Expressions

30. DAX Functions
• Aggregation
• SUM
• AVERAGE
• MIN
• MAX
• SUMX
• …
• Counting
• COUNT
• COUNTA
• COUNTBLANK
• COUNTROWS
• DISTINCTCOU
NT
• …
• Logic
• AND
• OR
• NOT
• IF
• IFERROR
• ….

31. More DAX Functions
• Information
• ISBLANK
• ISNUMBER
• ISTEXT
• ISNONTEXT
• ISERROR
• Text
• CONCATENTATE
• REPLACE
• SEARCH
• UPPER
• FIXED
• Time
• DATE
• HOUR
• NOW
• EOMONTH
• WEEKDAY

32. Backlog – Date Table
Date =
ADDCOLUMNS (
CALENDAR (DATE(2000;1;1); DATE(2025;12;31));
"DateAsInteger"; FORMAT ( [Date]; "YYYYMMDD" );
"Year"; YEAR ( [Date] );
"Monthnumber"; FORMAT ( [Date]; "MM" );
"YearMonthnumber"; FORMAT ( [Date]; "YYYY/MM" );
"YearMonthShort"; FORMAT ( [Date]; "YYYY/mmm" );
"MonthNameShort"; FORMAT ( [Date]; "mmm" );
"MonthNameLong"; FORMAT ( [Date]; "mmmm" );
"DayOfWeekNumber"; WEEKDAY ( [Date] );
"DayOfWeek"; FORMAT ( [Date]; "dddd" );
"DayOfWeekShort"; FORMAT ( [Date]; "ddd" );
"Quarter"; "Q" & FORMAT ( [Date]; "Q" );
"YearQuarter"; FORMAT ( [Date]; "YYYY" ) & "/Q" & FORMAT ( [Date]; "Q" )
)

33. DAX – Calculating a Backlog
• Add the following columns to the date table:
• ‘Opened’/’Closed’ –The number of tickets opened/closed on that day
• ‘TotalOpened’/’TotalClosed’ – Number of tickets opened/closed up to that day
• The Backlog is nowTotal Opened minusTotal Closed
Opened = CALCULATE(
COUNT(Tickets[TicketNr]);
FILTER(Tickets;Tickets[DatumAangemaakt]=EARLIER('Date'[Date]))
)
TotalOpened = CALCULATE(
SUM('Date'[Opened]);
ALL('Date’);
'Date'[Date]<=EARLIER('Date'[Date])
)

34. Adding MarketplaceVisuals

35. Calendar byTallan

36. Set up Forecast
• Important!Your time axis must have continuous dates

37. Setting up a Drillthrough Page
https://docs.microsoft.com/en-us/power-bi/desktop-drillthrough

38. Setting up Drillthrough
1. Add a detail page with the
visuals for the drillthrough
page
2. Add the field you want to
drill on in the ‘Drillthrough
filters’
3. Test the drilling from another
page

39. Analytics – Key Influencers

40. Key Influencers

41. Data
Product
Design &
Building
Expert
Evaluation
Data
Understanding
Business
Understanding
Data
Preparation
Deliver
Insights
01 02
03
0405
06
Understanding the business and it’s goals
Vision & Mission, Use Case Identification
Knowing the meaning of important data
Data Concepts & Model
Bringing together the data that matters
Data Quality / Cleaning / ETL
Designing data products that enable learning
Visualizations, Data Driven App, Analytic Model
Evaluating business value of data products key experts
Design Workshops for Stakeholder Feedback
Embedding data products to improve key processes
Publish, Share, Deploy
Qrisp BI – Our Analytics Flywheel
Based on CRISP-DM
Cross-Industry Standard for Data Mining

42. LoQutus Analytics & Insights
Kickstarting Analytics Choosing the right way to guide your analytics journey
Discover
Quick-scan of your
potential value in
analytics, your core
data assets, and the
key hurdles that lock
your data.
Kickstart
Do you have a data
challenge but don’t
know where to start?
We’ll kickstart your
analytics endeavors!
Foundation
The right
environment for
asking questions to all
your data assets, and
getting timely results.
BusinessValue
Information Architecture
Data Understanding
Data Exploration
Data Architecture
DataWarehouse
Data Pipelines
Data Lake
Data Preparation
Prototyping
Dashboards
Machine Learning