This document provides an agenda and overview for a workshop on introducing agile business intelligence sustainably. The workshop schedule includes sessions on what is business intelligence, introducing agile BI building blocks, building block details, BI-specific testing, and a retrospective. The presenter's slides and exercises are available online. The presenter's background and credentials in BI, agile methods, and various industry organizations are also provided.

1. My slides are / will be available for you at:
Introducing Agile Business
Intelligence Sustainably:
Implement the Right Building Blocks in
the Right Order
Raphael Branger, IT-Logix AG
Presentation: http://bit.ly/2zBpSvz
Exercises: http://bit.ly/2hvVVGF

2. Welcome & Overview of Workshop Schedule (14:25 – 14:30)
What is Business Intelligence? (14:30 – 15:10)
Introduction to the Agile BI Building Blocks (15:10 – 15:40)
Break (15:40 – 16:10)
Building Block details (16:10 – 17:10)
User Stories
BI-specific Testing
Retrospective (17:10 – 17:25)
Agenda
3

3. Raphael Branger, Senior BI Solution Architect, IT-Logix AG, Switzerland
Working in Business Intelligence & Data Warehousing since 2002
Looking at «Agile» in the context of BI since 2010
Actively contributing to the community…
http://rbranger.wordpress.com/ (English)
http://blog.it-logix.ch/author/raphael-branger/ (German)
Regular conference engagements
Follow me on Twitter: @rbranger
Member of…
TDWI www.tdwi.eu/ & https://tdwi.org
Disciplined Agile Consortium http://www.disciplinedagileconsortium.org/
Scrum Breakfast Club http://scrumbreakfast.club/
International Business Communication Standards (IBCS) Association http://www.ibcs-a.org
About me
6

4. What is Business Intelligence?

5. Grab some Post-its
Per note write down one key word or sentence of what you associate with BI & DWH
Does your company use BI & DWH?
Are you yourself an end user / developer etc. working with the BI & DWH system?
Any good or bad experience with BI & DWH systems?
…
After a few minutes, we will start to collect the notes and hear each ones short explanation.
What are your associations with Business Intelligence & Data Warehousing?

6. A typical BI asset
What do we need to build and
run this little dashboard app?
9

7. Per group of three or four, take 2
empty canvas sheets.
Take the pictures and try to stick them
to the appropriate place on one of the
canvas.
Take the text blocks and try to stick
them to the appropriate place on the
second canvas.
Timebox: 10 mins
Afterwards we’ll take some time to
discuss the BI overview together.
Exercise 1 «BI Overview»

8. 11
DWH
Integration
Data
Data Mart 1
Data Mart 2
Data Mart 3
Reports
Analysis
Dashboards
BI Strategy
Organisation & Processes
Data
Technical Metadata Business Metadata
Information
Market
BI Strategy
Vision
Mission
Objectives
Partial Strategies
BIStrategy
Management
Development
Operations
Governance
Inception Construction Transition
Process Metadata
BI Application
internal
external
Source
Systems
User
Internal
Users
External
Users
Customers
IT Strategy
Customers
Business Strategy
Data Science & AI
Data
Lake
Predictions
Ad-hoc
Operations

9. Agile BI Building Blocks

10. 14
IT-LOGIX AGILE BI BUILDING BLOCKS (V2.0 EN)
Agility
Amount of
upfront design needed
Basic
Infrastructure
Basic Patterns &
Standards
Agile Mindset & Organisation Agile Infrastructure & Patterns
Gulf
Chasm
«Tweaked» Waterfall
«Timeboxed» Iterations
«Lean» Development
Legend
Processes
& Organi-
sation
Develop-
ment
Methods
Techno-
logies
Values &
Principles
© by Raphael Branger, IT-Logix AG, www.it-logix.ch
Continuous Delivery

11. Take the overview sheet with the
numbered AgileBI Building Blocks.
Take one of the available building block
detail sheets.
Study the page and think about which
building block number is yours.
Once it is your turn, stick your page
near the corresponding block on the
wall.
We’ll briefly discuss the building block.
Exercise 2 «Agile BI Building Blocks»
15

12. BI specific User Stories

13. Implement Vertical Slices in Order to Prioritize.
Let’s have a look at two approaches regarding how to implement a BI system:
Only a vertical slicing of the implementation (aka Features) allows for ongoing (re-) priorization of
requirements.
Topic 1 Topic 1
Feature 1 Feature 2 Feature 3
10%
40%
70%
100%
Cumulative
Progress
Cumulative
Progress
50% 61% 100%
Connectivity
DWH
Data Mart
BI App
5%
25%
50%
10%
40%
70%

14. From Feature To User Story
Feature 1 User Stories
User Stories have a «a
priori» maximum duration –
e.g. 1 or 2 days. Why?
We force ourselves to a
more frequent and shorter
feedback cycle.
Short user stories are the
foundation to answer the
question if the project
progresses / «flows» as
desired or not.
RTS = Runnable & Tested
Stories are the real
progress indicator in a
project!
BI Application
DWH
Connectivity &
Infrastructure
Layer
▪ Report with monthly layout
▪ Report with weekly layout
▪ Report with variable measure selection
▪ …
▪ 1 fact table with a non-monetary measure (e.g.
quantity) + time dimension + product dimension
(without hierarchy)
▪ Additional measure
▪ Extend product dimension with a hierarchy
▪ …
▪ Setup Middleware
▪ Manual import
▪ Automated import
▪ …
BI Application Epics
DWH Epics
Connectivity &
Infrastructure Epics

15. Testing the User Story
Feature 1 User Stories
BI Application
DWH
Layer
▪ Direct within application
▪ Query in Excel
▪ Query in Excel
▪ Query in
database tool,
e.g.SQL Server Mgt. Studio
BI Application Epics
DWH Epics
Connectivity &
Infrastructure EpicsConnectivity &
Infrastructure

16. DWH
Gather together in teams of two to four
people.
Take the excercise sheet handed out.
Exercise 3 «BI User Stories»
20
FactEventParticipant
RegisterDate
EventID
ParticipantID
NoShow (Y/N)
(Count participants)
DimEvent
EventDate
Country
City
Venue Address
Location (Geo)
Max. Participants
DimDate_Register
DateValue
DimParticipant
Name
Member Category
Roundtable
Registration
System
(Web Service
or CSV export)
TDWI
Membership
System
(SQL Server)
Define at least three user stories. Remember the User
Story should be small enough to be implmented in 1
single day.
Timebox 10 minutes.
DWH
Automation
Tool
Feature 1

17. Feature (following the regular User Story schema):
As a TDWI Backoffice employee, I need to see the number of registered participants for a
Roundtable event so that I can organize the logistics for this event.
Connectivity Epic (following the FDD schema) (<action> the <result> <by|for|of|to> <object>)
Extract the event and participant data of the web based Roundtable Registration System to a CSV
file.
Connectivity User Story (following the FDD schema):
Manually export the event and participant data for all events to a CSV file.
Schedule and Save to FTP server the event and participant data for all events to a CSV file (on
the FTP server)
Download the event and participant data for all events to a local folder (on the DWH server)
Load the event and participant data for all events to a load table (1:1 copy with the DWH
Automation tool) in the DWH database.
Possible User Stories (Connectivity & Infrastructure)
21

18. Feature (following the regular User Story schema):
As a TDWI Backoffice employee, I need to see the number of registered participants for a Roundtable
event so that I can organize the logistics for this event.
DWH Epic (following the FDD schema) (<action> the <result> <by|for|of|to> <object>)
Model and load the event and participant data of the web based Roundtable Registration System to the
DWH and Data Mart.
DWH User Story (following the FDD schema):
Model and (full) load the event master data (without Location / Geo info, not historized) to DimEvent on
the DWH layer.
Model and (full) load the participant master data (without Member Category, not historized) to
DimParticipant on the DWH layer.
Model and (full) load the event registration transaction data to FactEventParticipant on the DWH layer.
Refactor the existing load implementation to allow for incremental loads.
Create and develop the data mart for FactEventParticipant, DimEvent and DimParticipant with
«Number of Participants» as its first measure.
Possible User Stories (DWH)
22

19. Feature (following the regular User Story schema):
As a TDWI Backoffice employee, I need to see the number of registered participants for a
Roundtable event so that I can organize the logistics for this event.
BI Application Epic (following the regular User Story schema)
As a TDWI Backoffice employee, I need a BI application to see the number of registered
participants for a Roundtable event so that I can organize the catering for this event.
BI Application User Story (following the regular User Story schema):
As a TDWI Backoffice employee I need to see the number of registered participants for the next
Roundtable in a selected location so that I can organize the catering for this event.
As a TDWI Backoffice employee I need to see the percentage of «No-Shows» for the past 10
roundtables in a selected location so that I can optimize the catering for upcoming events.
As a TDWI Backoffice employee, I need to be alerted if the number of participants for the next
Roundtable in any location is at 90% of the maximum capacity so that I can check if a larger room
is available.
Possible User Stories (BI Application)
23

20. BI specific Testing

21. Intra-System-Tests
Where do we test? (1/2)
Each system component is tested on its own.
Staging Data
Warehouse
Reports
Source System
ETL
Marts
Cubes
Semantic Layer
Testing
Testing TestingTesting Testing Testing

22. Where do we test (2/2)
An external test tool acts independant from the system and its properties (and eventually errors).
Staging Data
Warehouse
Reports
Source System
ETL
Marts
Cubes
Semantic Layer
Testing
Inter-System-Tests

23. Test Approaches
Manually in combination with checklists & forms
Classical test automation solutions to test the
GUI, performance etc.
How do we test?
Functional  specific software “functions”
Start client software
Login to BI system
Edit report
Non-Functional  more quality oriented
features like
Performance
Usability
(Security)

24. Frist Time vs. Regression
First Time Tests
Regression Tests
Manual Testing
Automated Testing

25. Information Products
(e.g. reports, dashboards etc.)
Test the structure based on
metadata
Test the data based on
testdata and the information
product
Test the layout by comparing
a reference layout with the
information product
Testing per Architecture Layer - Frontend
Manual
Cell based comparison

26. Information Products
(e.g. reports, dashboards etc.)
Test the structure based on
metadata
Test the data based on
testdata and the information
product
Test the layout by comparing
a reference layout with the
information product
Testing per Architecture Layer - Frontend
Manual Automated
BI vendor specific
Generic (PDF, XLS, XML…)
Cell based comparison
Optical comparison

27. Tables in the different layers
(Source, Staging, DWH, Data
Mart, …)
Test the structure based on
metadata (DB schema)
Test the data based on
comparison data and actual
values
Testing the performance
Testing per Architecture Layer - Backend
Manual Automatisiert
DWH specific toolsCell based comparison
SQL based comparison
Source: https://bigeval.com/en/data-warehouse-etl-testing/

28. Test cases…
… contain one or more test objects (e.g. a report, measure, data set)
… need one or more reference objects
... presuppose a congruent data foundation for reference and test objects, that means the data is
either stable or develops itself further synchronously.
Stable: Define a data set which isn’t changed anymore, e.g. a closed time period.
Dynamic: Comparison data is refreshed regularly.
Test Case Design

29. Alternative 1: There is a test source system on which any test cases can be simulated.
Alternative 2: Take the production source system
Alternative 3: Fictitious source data are generated in the DWH, e.g. on the stage layer.
Testing with test data – where to take it from?

30. How big is the amount of test data?
Detail
Analysis
Modelling &
ETL Code
BI
Application
Full Data
Testing
1 day – 1 week 1 day – 1 week 1 day – 1 week 1 day – 1 week
Product Owner
Define test data set Unit Testing with
test data set
Integration Testing
with full data set
Connectivity
& DWH
Stories
BI Application
stories
Feature 1

31. Testing is a crucial success factor of every BI / DWH system.
Testing should be a «built-in» part of every BI / DWH architecture.
The more tests you have, the more meaningful is test automation.
Data based testing is not exactly the same as testing «classical» GUI oriented software: Adapt where
possible, be creative where necessary.
There are BI specific testing tools.
BI specific testing: For you to take away:

32. Retrospektive

33. Write down your lesson’s learned – what do you take with you? (Timebox 3 minutes)
Share lessons learned (Timebox 10 minutes)
Retrospective
37

34. References und Literature
With friendly support from:
IT-Logix Team (http://www.it-logix.ch)
BiGeval Team (http://www.bigeval.com)
Wherescape Team (http://www.wherescape.com)
Tricentis Team (http://www.tricentis.com)
GB&Smith Team (http://www.gbandsmith.com)
Scott Ambler (http://www.disciplinedagiledelivery.com)
Lawrence Corr (http://www.modelstorming.com)
Peter Stevens (https://scrumbreakfast.club)
Maturity Model Inspiration: Belshee Arlo: Agile Engineering Fluency
http://arlobelshee.github.io/AgileEngineeringFluency/Stages_of_practice_map
.html
Literature:
Branger Raphael, Bausteine für agile und nachhaltige BI,
BI Spektrum, 5. Ausgabe 2015, SIGS DATACOM
http://www.tdwi.eu/fileadmin/user_upload/zeitschriften//2015/05/brang
er_BIS_05_2015_dzer.pdf
Collier Ken, Agile Analytics, Addison-Wesley, 2012
Corr Lawrence, Stagnitto Jim: Agile Data Warehouse Design:
Collaborative Dimensional Modeling, from Whiteboard to Star
Schema, DecisionOne Press, 2011
Hughes Ralph: Agile Data Warehousing Project Management:
Business Intelligence Systems Using Scrum, Morgan Kaufmann, 2012
Ambler Scott W., Lines Mark: Disciplined Agile Delivery: A
Practitioner's Guide to Agile Software Delivery in the Enterprise, IBM
Press, 2012
Ambler Scott W., Sadalage Pramod J.: Refactoring Databases:
Evolutionary Database Design, Addison-Wesley Professional, 2006
Krawatzeck Robert, Zimmer Michael, Trahasch Stephan, Gansor
Tom: Agile BI ist in der Praxis angekommen, in: BI-SPEKTRUM
04/2014
Memorandum für Agile Business Intelligence:
http://www.tdwi.eu/wissen/agile-bi/memorandum/
Oliver Cramer, Data Warehouse Automation, 32. TDWI Roundtable in
Zürich, 2015
Agile in a nutshell: http://blog.crisp.se/2016/10/09/miakolmodin/poster-
on-agile-in-a-nutshell-with-a-spice-of-lean

35. Blogs and Webpages around Data Warehouse Automation
TDWI E-Book Data Warehouse Automation: https://cdn2.hubspot.net/hubfs/461944/downloads/Analyst_Reports/TDWI_ebook_Accelerating_Business.pdf
Barry Devlin: BI, Built to Order, On-demand: Automating data warehouse delivery: http://www.9sight.com/2015/01/wp-built-to-order/
Oliver Cramer: Prinzipien der Data Warehouse Automation und grober Marktüberblick:
http://ddvug.de/wp-content/uploads/4_Tagung_der_DDVUG_Prinzipien_der_Data_Warehouse_Automation_Handout.pdf
Eckerson Group: Data Warehouse Automation Tools: https://www.wherescape.com/media/1791/eckerson-group-dw-automation-tools-report.pdf
What is Data Warehouse Automation: https://www.wherescape.com/products-services/what-is-data-warehouse-automation/
WhereScape RED Product Information: https://www.wherescape.com/products-services/wherescape-red/
WhereScape 3D Product Information: https://www.wherescape.com/media/1590/wherescape-3d-data-sheet.pdf
39

37. «Traditional projects start with requirements
and end with data.
Data Warehousing projects start with data and
end with requirements.»
Bill Inmon
Raphael Branger, Senior Solution Architect & Partner
rbranger@it-logix.ch
Follow us: @rbranger / @itlogixag
DE: http://blog.it-logix.ch/author/raphael-branger
EN: http://rbranger.wordpress.com