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@SRogalsky
WinnipegAgilist@blogspot.com
Steve.Rogalsky@protegra.com
Steve Rogalsky
Tom DeMarco
Incremental
Iterative
http://blog.crisp.se/2016/01/25/henrikkniberg/making-sen
Incremental
Iterative
http://blog.crisp.se/2016/01/25/henrikkniberg/making-sen
“Deliver the smallest thing you
can think of that will get the
customer testing things and
giving us feedback” - Henrik
Iterative
1 2 3 4 5
Credit: Jeff Patton
Incremental
This type of iterative approach enables
you to deliver value early and often
Some examples
Example: Objective vs. Subjective
Quality
Release 1: Single, No kids, Dental Coverage Only, no Life & Disability
Example: By market segment
Example: Mobile Conference
App
Example: Blue Canvas Payroll
Example: TheLocalFrequency.com
Example: Leasing System
“You can’t terminate a lease
until you can create one”
Example: Wizard of Oz
I have a great idea!
• Review the materials
• Designate one or more product owner(s) in
your group
• Copy the first 2 rows of the story map (next
slide…)
“I have a great idea!”Your Turn!
Manage
Users
Manage
Content
Register Login Modify
Profile
Post
Recipe
Edit
Recipe
Attach
Photos
Personalize Dashboard
View
favourites
Meal
planning
Create
grocery
list
Find Sales My recipes My stats My
history
Recipe
Search
Search for
Recipe
Scale
recipes
Rate/com
ment on
recipes
Share
recipes
View
author
profile
Your (draft) User Story Map
Finding your first slice
Feature Mining
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Jobs
• What job(s) is your product being hired for?
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Value?
• Where is the value in this application?
• Why is this worth doing?
• How can we make money?
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Size
• What makes this big?
• “Why isn’t this something we could just knock
out in an afternoon”?
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Risk
• What parts keep you up at night?
• Where could this fail?
• Where is the technical risk?
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Uncertainty
• Where is the uncertainty?
• What assumptions are we making?
• What do we need to learn?
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Prioritize
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Brainstorm
• Generate options: What could your
skateboard be??
Jobs Value Size
Risk
Uncertainty
Credit: Feature Mining by Richard Lawrence (modified slightly)
Agileforall.com/course/8020-product-ownership
CODE: STEVESENTME = 10% discount
In Closing
To find your first slice:
- Generate the skeleton of your
Story Map
- Consider Together:
- Jobs, Value, Size, Risk, &
Uncertainty
@Srogalsky & @protegra
WinnipegAgilist@blogspot.com
Steve.Rogalsky@protegra.com
Steve Rogalsky
Thanks!
Questions?
http://www.slideshare.net/SteveRogalsky/story-maps-episode-iv-a-new-slice
A final example
Credits
• User Story mapping:
– http://winnipegagilist.blogspot.ca/2012/03/how-to-create-user-story-map.html
• Tom DeMarco quote & article:
– https://www.computer.org/cms/Computer.org/ComputingNow/homepage/2009/0709/rW_SO_Viewpoints.pdf
• Making sense of MVP (Henrik Kniberg)
– http://blog.crisp.se/2016/01/25/henrikkniberg/making-sense-of-mvp
• Feature Mining:
– Richard Lawrence: http://agileforall.com/course/8020-product-ownership/
• Other interesting links
– "How Thin is Thin?" An Example of Effective Story Slicing (Dave Rooney):
http://blog.daverooney.ca/2014/08/how-thin-is-thin-example-of-effective.html
– How to split a user story (Richard Lawrence):
http://agileforall.com/resources/how-to-split-a-user-story/
• Images:
– Death Star: http://www.starwars.com
– Thermal Detonator: http://vignette3.wikia.nocookie.net/starwars
– Wizard of Oz:
https://theyellowbrickroadfreeblog.files.wordpress.com/2013/04/wizardofoz_4675.jp
g

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