How to Do User Story Mapping

07/14/15 • 51 min

Jeff Patton, author of User Story Mapping, teaches us how to map user stories by focusing on the user's journey to an outcome. He shares his opinion on the notorious "MVP" and how he helped Gary Levitt build his MVP with Mad Mimi.

Here's what to listen for:

00:49 What is a user story?
02:07 What does a user story look like?
02:57 When people refer to user stories do they mean the documentation around the conversations they’ve had?
03:44 Why is just having stories written down in a document not sufficient?
05:47 What is a good user story template?
09:17 What was the motivation for writing User Story Mapping?
11:44 Is the concept of a “map” about the narrative of a user’s journey?
17:36 How did Jeff help Gary from Mad Mimi get clarity on what he was doing?
20:54 Why were things taking longer for Gary when he came to you?
23:31 What does Jeff’s road mapping process look like?
26:47 What was it about Jeff and Gary’s conversation that took him from having a giant backlog to organizing user stories?
29:36 What is your definition of a “minimum viable product” (MVP)?
34:41 Why do you want to build something “less than minimal” before building the MVP?
39:34 Why is so difficult to put a time estimate on when software will be done?
44:53 What is meant by “scope doesn’t creep, understanding grows”?

Here's what to listen for:

00:49 What is a user story?
02:07 What does a user story look like?
02:57 When people refer to user stories do they mean the documentation around the conversations they’ve had?
03:44 Why is just having stories written down in a document not sufficient?
05:47 What is a good user story template?
09:17 What was the motivation for writing User Story Mapping?
11:44 Is the concept of a “map” about the narrative of a user’s journey?
17:36 How did Jeff help Gary from Mad Mimi get clarity on what he was doing?
20:54 Why were things taking longer for Gary when he came to you?
23:31 What does Jeff’s road mapping process look like?
26:47 What was it about Jeff and Gary’s conversation that took him from having a giant backlog to organizing user stories?
29:36 What is your definition of a “minimum viable product” (MVP)?
34:41 Why do you want to build something “less than minimal” before building the MVP?
39:34 Why is so difficult to put a time estimate on when software will be done?
44:53 What is meant by “scope doesn’t creep, understanding grows”?

Previous Episode

How to Keep Code Quality High

Michael Bernstein of Code Climate explains how to monitor your code's quality with static analysis. He tells us how you can maintain or improve quality over time, and what you can do to fix poor code.

Here's what to listen for:

00:44 What is high quality code?
02:06 What is code that’s easy to read?
04:12 What is overly clever code?
06:44 What is the danger of overly clever code?
07:30 Is code able to document itself?
09:47 When do developers read code?
10:40 Why do people spend more time reading code than writing it?
12:42 What are some common qualities that make for lower-quality code?
15:09 What is a test?
16:50 What is modular code?
17:57 What is unmodular code?
18:37 What is static analysis?
20:56 What does a static analysis tool do?
21:34 What do I do when I receive a poor Code Climate score?
24:25 How do I prioritize changes to my codebase?
29:59 What are hot spots in Code Climate?
32:07 What does Code Climate’s test coverage feature do?
33:45 How do you determine how good your tests are?
34:51 What is a good unit test?
37:09 What is mob refactoring?
37:43 Can you do mob refactoring remotely?

Next Episode

The Rise of the Data Scientist

Jonathan Cornelissen tells us about DataCamp, the need for data scientists, and how to become one yourself. We also learn about some popular languages and libraries for analyzing data.

Here's what to listen for:

00:43 What is the story behind DataCamp?
02:06 What is data science?
02:52 What kind of xdata is out there that can be analyzed?
04:46 Do I need a scientific or statistical background to work with data science?
05:26 Does DataCamp help establish a theoretical background?
06:21 Do only big companies need data science?
07:16 What is big data?
07:58 Can the term big data be used interchangeably with data science?
09:08 Do you need a “billion dollar budget” to build a data science team? What kind of people do I need to build that kind of team?
12:08 What is behind the shortage of data scientists?
12:48 What can a startup do to incorporate data science into their team?
13:45 What is meant by data savvy?
14:10 What do you do with the data once it’s collected?
14:50 What is cohort analysis?
15:42 Once users are segmented, what could you do at that point?
16:21 Are correlations the primary sort of analysis?
17:14 Are people trying to make causative claims out of correlative data?
18:23 What are some other examples of techniques in addition to correlation?
18:55 Are there any other interesting algorithms out there that people are using?
20:07 Are these analyses run offline or real-time?
20:37 What is the Spark framework?
21:10 What is the R language?
24:09 Where does R fit in in a company?
24:47 Is R being run by a human or is there also a sense of R running on the server to serve up recommendations?
25:30 Is R still evolving as a language?
25:58 Is there anything people should try to learn before trying to tackle R as a language?
26:52 Why learn a language like R?
28:27 Does R allow you the ability to communicate the insights that you’re getting from the data that you’ve analyzed to build a narrative to help the non-technical people on your team?
29:19 Is visualizing the data that we get back important to our understanding of that data? Why?
29:57 Does DataCamp help people visualize data?
30:51 Aside from R, what other tools are out there that a data scientist would use?
31:23 What is Hadoop?
33:09 What is the concept of MapReduce?
33:42 What is the mark of a good data scientist?
35:30 Why do you need domain expertise?
38:30 How are people becoming aware of data science? Where do these people start?