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Fail Fast, Iterate
Quickly with
Power BI &
Google Analytics
Yoann Jeffrey CLOMBE
Agenda
I) Introduction & concepts : Self-service BI
architecture.
II) Real life example : BDC return on
experience of a self service BI project.
III) DEMO – Deeper Dive into Power BI:
- Dashboards Google Fabrics - BDC Mobile.
- Dashboards DAX Calculations & Running
Totals.
- Dashboards Google Analytics Data / Geo
Map BDC.
- Dashboards from Web Service.
Yoann CLOMBE
 Data Analyst @SII – Canada /
Business France (IEC Program)
 Blogger @ExcelTrail :
exceltrail.wordpress.com
 Twitter @DataZouk
About the Speaker
About the Business Development Bank
of Canada (BDC)
I) Introduction &
Concepts
Model, tools, processes,
methods
Steps to prepare Data !
1. Discover
2. Structure
3. Clean
4. Enrich
5. Validate
6. Publish
Six steps to implement self-service
Step 1 : Classify Business Users
Six steps to implement self-service
Step 2 : Define Self-service Workflows
Six steps to implement self-service
Step 3 : Define Permissions
Six steps to implement self-service
Step 4 : Create a data governance committee
Six steps to implement self-service
Step 5 : Establish Reporting Workflows
Six steps to implement self-service
Step 6 : Continuously educate, train and support
Self service solutions actors
Benchmark of Self Service BI Solutions
Power BI Tableau Qlik MicroStrategy
Data Capacity 10 Go per
user
N/A 10 Go per
user
Data
Sources
Mobility
Security Azure AD,
Identity
Mgmt
SAML 2.0
Auth
End to end
data encrypt
SAML 2.0 ,
SSO
Free version 1Go per
user
limitation
Free 14
days
version
250 Mo
Data
limitation ( 5
users)
1 year
evaluation
version
Price 10 $ / user /
months
500$ / user /
year
20$ / user /
months
600$/ user/
year
Why consider Power BI ?
Data
discovery
Data
preparation
Data
visualization
Publish,
share,
collaborate
Power BI desktop Power BI 365
Free License required
Schedule data refresh
Quick insights
R integration soon integration with Python
 Price : 10$ per user per months.
 Integration with Office 365 (Excel, Active Directory)
 Open Source Community (Python, R, D3.js)
II) Real Life
Example: Mobile
Client Facing
Employees with
Power BI
Return on experience
 Objectives :
Who ? To identify who’s using the applications
Where ? To identify in which region and which area
employees are using the applications.
Why? How ? To monitor employees usage metrics to
improve best practices.
Context
Problem / Solution / Results
Problem Solution Results
Siloed data across BDC’s
Mobile Department made it
difficult for BDC’s mobile &
finance teams to intelligently
analyze Mobile Apps Usage &
Retention to deliver statistics to
the business (Sales / Marketing
/ Change Management).
BDC needs to increase
revenue coming from actions
delivered by certain
applications.
How to retain adoption with
other applications by increasing
the number of interaction with
Mobile Apps ?
Leveraged Power BI rich
analytics capabilities & features
to create maps & operational
reports that deliver real-time
insights and value for the
business.
Cost effective : after Power BI
Cloud assessment it have been
decided to move from
standalone version to Power BI
Prenium ( 6100$ / months for
the Mobility Team).
Used Power BI flexible Data
Model to build a unified view of
the bank’s Mobile Operation
that brings together Google
Analytics Data, On premises
Data & other data sources.
A single views of Mobility Client
Facing Team is now available
to all Business Analysts to help
them analyze & monitor
applications’ usage & adoption
towards BDC’s surcursales.
Adoption of Power BI Self-
service Solution for other
departments.
Organisation of Power Users
Teams { Data Analysts – Data
Scientists }
By the Data Management /
Governance team.
Power BI Premium cost estimate for
Mobile Client Facing & Finance
Self-service analytic platform
Before MCFE Project
Sources Sandbox Self-service
External
data
BI
DataMart's
Excel
Power
BI
SSIS R Microstrategy
SAP
B.O.
Others…
Data
discovery
Data
preparation
Data
visualization
Publish/
collaborate
Self-service
needs
Analytic platform
Operationalize
Business units IT/BI
TIDAL
Email ExcelPDF
SSIS Informatica
Source
systems
Business units
Consume
Power BI
365
Power BI 365
content packs
Governance
Previous BI solutions architecture
Before MCFE Project
Self-service ETL
Find, Explore, Profile, Transform
Publish, Share
ETL
Self-service
Visualization
Publish, Share
results
Self-service
Analytics
SANDBOX
Manual refresh
Manual refresh
x VDI
SQL Server
Management
Studio
Self-service analytic platform
After MCFE Project
Sources Sandbox Self-service
External
data
BI
DataMart's
Data
discovery
Data
preparation
Data
visualization
Operationalize
Self-service
needs
Analytic
platform
Publish/ collaborate/
consume
Governance
Business units
One solution
(POWER BI)
Current Self-service
BI solutions architecture
Prepare
Find, Explore, Profile, Transform
Store
Publish
Analytics
sandbox
Self-service BI solutions architecture
for mobility
Discover
Find, Explore, Profile, Transform
Store Visualize
SA N D B OX
Fabric
Doc+
Meeting
RSL
Proposal
FastTrack
Client 360
Mobile apps
Data sources
Desktop
client
Web
API
development No backup
Self Service BI Solution architecture
BI Solution
architecture
27
Deeper Dive into
Power BI
DEMO
 Power Query
 Dashboards Google Fabrics with Power BI
Dashboards Google Fabrics BDC
Mobile.
Dashboards DAX Calculations &
Running Totals.
Dashboards Google Analytics Data /
Geo Map BDC
 Shazal GOMES : Architect of analytical solutions
@Business Development Bank of Canada (BDC).
 SQLbi.com team (Marco Russo , Alberto Ferrari)
books and workshops.
 Exceleratorbi.com.au, Matt Allington and his book :
Learn to write DAX.
Special Thanks to
bdc.ca
1-888-INFO-BDC
BDC
BDC_ca
BDC
Thank you.
What questions would you like to ask
me now ?
Yoann Jeffrey CLOMBE
The key to success is failure.

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Yoann Clombe : Fail fast, iterate quickly with power bi and google analytics

  • 1. Fail Fast, Iterate Quickly with Power BI & Google Analytics Yoann Jeffrey CLOMBE
  • 2. Agenda I) Introduction & concepts : Self-service BI architecture. II) Real life example : BDC return on experience of a self service BI project. III) DEMO – Deeper Dive into Power BI: - Dashboards Google Fabrics - BDC Mobile. - Dashboards DAX Calculations & Running Totals. - Dashboards Google Analytics Data / Geo Map BDC. - Dashboards from Web Service.
  • 3. Yoann CLOMBE  Data Analyst @SII – Canada / Business France (IEC Program)  Blogger @ExcelTrail : exceltrail.wordpress.com  Twitter @DataZouk About the Speaker
  • 4. About the Business Development Bank of Canada (BDC)
  • 5. I) Introduction & Concepts Model, tools, processes, methods
  • 6. Steps to prepare Data ! 1. Discover 2. Structure 3. Clean 4. Enrich 5. Validate 6. Publish
  • 7. Six steps to implement self-service Step 1 : Classify Business Users
  • 8. Six steps to implement self-service Step 2 : Define Self-service Workflows
  • 9. Six steps to implement self-service Step 3 : Define Permissions
  • 10. Six steps to implement self-service Step 4 : Create a data governance committee
  • 11. Six steps to implement self-service Step 5 : Establish Reporting Workflows
  • 12. Six steps to implement self-service Step 6 : Continuously educate, train and support
  • 14. Benchmark of Self Service BI Solutions Power BI Tableau Qlik MicroStrategy Data Capacity 10 Go per user N/A 10 Go per user Data Sources Mobility Security Azure AD, Identity Mgmt SAML 2.0 Auth End to end data encrypt SAML 2.0 , SSO Free version 1Go per user limitation Free 14 days version 250 Mo Data limitation ( 5 users) 1 year evaluation version Price 10 $ / user / months 500$ / user / year 20$ / user / months 600$/ user/ year
  • 15. Why consider Power BI ? Data discovery Data preparation Data visualization Publish, share, collaborate Power BI desktop Power BI 365 Free License required Schedule data refresh Quick insights R integration soon integration with Python  Price : 10$ per user per months.  Integration with Office 365 (Excel, Active Directory)  Open Source Community (Python, R, D3.js)
  • 16. II) Real Life Example: Mobile Client Facing Employees with Power BI Return on experience
  • 17.  Objectives : Who ? To identify who’s using the applications Where ? To identify in which region and which area employees are using the applications. Why? How ? To monitor employees usage metrics to improve best practices. Context
  • 18. Problem / Solution / Results Problem Solution Results Siloed data across BDC’s Mobile Department made it difficult for BDC’s mobile & finance teams to intelligently analyze Mobile Apps Usage & Retention to deliver statistics to the business (Sales / Marketing / Change Management). BDC needs to increase revenue coming from actions delivered by certain applications. How to retain adoption with other applications by increasing the number of interaction with Mobile Apps ? Leveraged Power BI rich analytics capabilities & features to create maps & operational reports that deliver real-time insights and value for the business. Cost effective : after Power BI Cloud assessment it have been decided to move from standalone version to Power BI Prenium ( 6100$ / months for the Mobility Team). Used Power BI flexible Data Model to build a unified view of the bank’s Mobile Operation that brings together Google Analytics Data, On premises Data & other data sources. A single views of Mobility Client Facing Team is now available to all Business Analysts to help them analyze & monitor applications’ usage & adoption towards BDC’s surcursales. Adoption of Power BI Self- service Solution for other departments. Organisation of Power Users Teams { Data Analysts – Data Scientists } By the Data Management / Governance team.
  • 19. Power BI Premium cost estimate for Mobile Client Facing & Finance
  • 20. Self-service analytic platform Before MCFE Project Sources Sandbox Self-service External data BI DataMart's Excel Power BI SSIS R Microstrategy SAP B.O. Others… Data discovery Data preparation Data visualization Publish/ collaborate Self-service needs Analytic platform Operationalize Business units IT/BI TIDAL Email ExcelPDF SSIS Informatica Source systems Business units Consume Power BI 365 Power BI 365 content packs Governance
  • 21. Previous BI solutions architecture Before MCFE Project Self-service ETL Find, Explore, Profile, Transform Publish, Share ETL Self-service Visualization Publish, Share results Self-service Analytics SANDBOX Manual refresh Manual refresh x VDI SQL Server Management Studio
  • 22. Self-service analytic platform After MCFE Project Sources Sandbox Self-service External data BI DataMart's Data discovery Data preparation Data visualization Operationalize Self-service needs Analytic platform Publish/ collaborate/ consume Governance Business units One solution (POWER BI)
  • 23. Current Self-service BI solutions architecture Prepare Find, Explore, Profile, Transform Store Publish Analytics sandbox
  • 24. Self-service BI solutions architecture for mobility Discover Find, Explore, Profile, Transform Store Visualize SA N D B OX Fabric Doc+ Meeting RSL Proposal FastTrack Client 360 Mobile apps Data sources Desktop client Web API development No backup
  • 25. Self Service BI Solution architecture
  • 28.  Power Query  Dashboards Google Fabrics with Power BI Dashboards Google Fabrics BDC Mobile.
  • 29. Dashboards DAX Calculations & Running Totals.
  • 30. Dashboards Google Analytics Data / Geo Map BDC
  • 31.  Shazal GOMES : Architect of analytical solutions @Business Development Bank of Canada (BDC).  SQLbi.com team (Marco Russo , Alberto Ferrari) books and workshops.  Exceleratorbi.com.au, Matt Allington and his book : Learn to write DAX. Special Thanks to
  • 32. bdc.ca 1-888-INFO-BDC BDC BDC_ca BDC Thank you. What questions would you like to ask me now ? Yoann Jeffrey CLOMBE The key to success is failure.

Editor's Notes

  • #8: Identify all business users in your organization who use data and classify them based on their information requirements. Casual users use data to do their jobs, while power users are hired to collect and analyze data full time.
  • #9: Create iterative workflows that define how data flows between and within stages. Workflows are best implemented using a standard data and analytic platform.
  • #10: Administrators define data access, authoring, and sharing rights for each user. Having a standard data and analytics platforms simplifies this process.
  • #11: Establish a data governance committee to review ad hoc reports and data elements designed for broad-based consumption. If approved, reports and models can be reviewed or operationalized by a data team. Permissions baked into data and analytic platforms can automate many governance decisions.
  • #12: Organizations need to establish both governed and ungoverned flows of reports, dashboards, and underlying models. Governed reports should carry a watermark or seal to distinguish them from ad hoc reports.