why ask why? try agile bi!
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VOTE on the Biggest BI Challenges
Welcome!
As you’re getting settled, take a
minute to put a dot on the board
by what you think are the three
biggest BI challenges.
Why Ask WhyTry Agile BI
November 5, 2014
Agenda
• Agile BI Definition
• Assess Your Current State
• Then…
– Select an Agile Methodology
– Have a Kickoff
– Inspect and Adapt
BI
BI encompasses all aspects of a system
needed to produce meaningful information
to drive data driven decision making
– Data Processing (Cleansing, Transforming,
Loading)
– Data Architecture and Warehousing
– Data Analysis and Visualization Tools
Agile
Agile BI
Applying an Agile mindset to business intelligence
• Using an iterative, incremental, evolutionary
approach
• Focusing on value-driven development
• Delivering production quality applications
• Using barely sufficient processes
• Automating everything
• Collaborating with the customer
• Encouraging self-organizing and self managing
teams
Why Agile?
Source: http://www.versionone.com/Agile101/Agile-Software-Development-Benefits/
The people who need
the data see the data
You need different
data? Sure!
The data source
changed? We’re on it!
Business Intelligence,
enough said
Fail fast
Agile BI Maturity ModelTeam Roles /
Skill Sets
Technical
Architecture
Engineering
Practices
*All team members can independently
complete any task from database
design to report creation
*It’s not about getting your job done it’s
about getting the job done
*Increased collaboration
Example: ETL developers work with
data modelers to come up with a
database design that balances the
tradeoffs between reporting and
loading
*Decreased formality in interactions
across skill sets
*Collaboration among people with the
same skill set - Example: data
modelers work with other data
modelers
*Official transitions and likely
disagreement across skill sets -
Example: ETL developers are given
source to target mappings when the
data modelers complete the database
design and are upset that the design is
hard to load
*Clear understanding of data’s
business value
*Clear understanding of the purpose for
each component of the technical
architecture
*Active effort to clarify understanding of
data’s business value
*Streamlined architecture where
possible
*Process to deprecate unused
components
*Numerous (possibly) redundant layers
(staging, ODS, EDW, data marts, etc.)
*Inclusion of data with no clear
business value
*Lingering tables, reports, ETL scripts,
with no known purpose
*End-to-end use of optimal engineering
practices
*Team self-enforces usage through
criteria for completing work
*Some configuration management
(SQL scripts to create all db objects are
under CM, but not ETL and report
information)
*Some automation is in place (perhaps
to promote new objects or code to
another environment or to test ETL)
Leve
l 3
Leve
l 2
Leve
l 1
Assess Your Current State
• How well is your team setup for
collaboration and change?
Agile BI Maturity Model
Team Roles / Skill Sets
It’s not about getting your job done
it’s about getting the job doneLevel
3
Level
2
Level
1
Collaboration Formality
ETL ReportingData
ModelersDBAs
etc.
https://www.castlellc.com/collaboration.aspx
Agile BI Maturity Model
Create a dedicated team with skills needed to get data
into the hands of end users to make decisions
Team Roles / Skill Sets
Support self organized culture
- Let the team define their own success criteria
- Avoid saying HOW things must be done
Fill skill set gaps with external training, cross training,
lunch and learns and more
Level
3
Level
2
Level
1
Assess Your Current State
• How well is your team setup for
collaboration and change?
Assess Your Current State
• What is your current technical
architecture? What aspects present the
biggest challenges to incremental
evolution and change?
Avoidable Inevitable
Change Is…• Grain of fact table
• New type 2 attribute
• Change from type 1 to type 2
• Multi-purpose column or table
• Redundant data
• Tables with too many columns or rows
• “Smart” columns
• Complex ETL objects
• Large SQL modules
• Unconformed Dimensions
• Indiscriminate use of materialized views
• Underutilization of materialized views
• Overreliance on documentation
Agile BI Maturity Model
Technical Architecture
Level
3
Level
2
Level
1 Purpose?? Value??
Purpose? Value?
Purpose! Value!
Agile BI Maturity Model
* Identify redundancy
* Combine or streamline things where possible
Technical Architecture
Keep it up! Don’t let complexity creep in.
*Create a central repository
*Get rid of things that are no longer being used
Level
3
Level
2
Level
1
Assess Your Current State
• What is your current technical
architecture? What aspects present the
biggest challenges to incremental
evolution and change?
Assess Your Current State
• Do you follow technical practices that can
enable agility?
Agile BI Maturity Model
Engineering Practices
Level
3
Level
2
Level
1
• No central location for system building
blocks
• Manual push between environments
• Some configuration management
• Some automation
End-to-end use of optimal engineering practices
Agile BI Maturity Model
*Start putting files into a configuration management
system
*Work out the kinks of your deployment process
Engineering Practices
*Hold yourselves accountable for maintaining high
standards for new efforts
*Reduce technical debt each iteration
*Start creating automated tests
Level
3
Level
2
Level
1
Assess Your Current State
• Do you follow technical practices that can
enable agility?
Agile BI Maturity ModelTeam Roles /
Skill Sets
Technical
Architecture
Engineering
Practices
*All team members can independently
complete any task from database
design to report creation
*It’s not about getting your job done it’s
about getting the job done
*Increased collaboration
Example: ETL developers work with
data modelers to come up with a
database design that balances the
tradeoffs between reporting and
loading
*Decreased formality in interactions
across skill sets
*Collaboration among people with the
same skill set - Example: data modelers
work with other data modelers
*Official transitions and likely
disagreement across skill sets - Example:
ETL developers are given source to
target mappings when the data modelers
complete the database design and are
upset that the design is hard to load
*Clear understanding of data’s
business value
*Clear understanding of the purpose for
each component of the technical
architecture
*Active effort to clarify understanding of
data’s business value
*Streamlined architecture where
possible
*Process to deprecate unused
components
*Numerous (possibly) redundant layers
(staging, ODS, EDW, data marts, etc.)
*Inclusion of data with no clear
business value
*Lingering tables, reports, ETL scripts,
with no known purpose
*End-to-end use of optimal engineering
practices
*Team self-enforces usage through
criteria for completing work
*Some configuration management
(SQL scripts to create all db objects are
under CM, but not ETL and report
information)
*Some automation is in place (perhaps
to promote new objects or code to
another environment or to test ETL)
*Building blocks of the system (db
create scripts, ETL packages, report
files, etc.) are not maintained in any
central location nor are they under
configuration management
*Files are manually copied from one
environment to another
Leve
l 3
Leve
l 2
Leve
l 1
Select an Agile Methodology
Scrum
Kanban
RU
P
XP
BDD
And so
on…
Scrum
Have a Kick Off
Inspect and Adapt
Contact InformationSara Handel
sara.handel@excella.com
Agile BI Training – December 11, 2014
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