The document outlines the importance of data strategy in project management, emphasizing that two-thirds of projects require a solid framework for data analysis. It advocates for a structured approach to data collection and visualization to derive meaningful insights that align with business goals. The document also categorizes data into quantitative and qualitative types, providing a roadmap for implementing data-driven decisions through iterative processes like design sprints.

1. Data Strategy
Product Management

2. Projects & Pain
• 2/3 of my projects required a data strategy
• Spoke with Developers and Designers about
their pains
• “Clients want a magic dashboard with correct
answers”

3. Solution
• Formalize the framework and a deliverable
• Framework = “measure, learn and build”
• Deliverable = strategic/ tactical dashboard

4. Create ideas /
make
suggestions
Ask questions /
create
hypotheses
BuildCollect data
Analyze data

5. Analytics
The discovery and communication of meaningful
patterns in data.

6. Analytics
The discovery and communication of meaningful
patterns in data.
Analytics starts with collecting and
visualizing data.

7. Analytics
The discovery and communication of meaningful
patterns in data.
Patterns start to emerge after
slicing, dicing and comparing.

8. Analytics
The discovery and communication of meaningful
patterns in data.
Assignment of meaning to trends requires
an understanding of the business goals.

9. Analytics
The discovery and communication of meaningful
patterns in data.
Once you’ve discovered the meaning of trends in
your data, you have information that you can act on.

10. Why data?
There are things we know we know
There are things we know we don’t know
There are things we don’t know we know
There are things we don’t know we don’t know
Thanks, Donald Rumsfeld and Lean Analytics

11. Why data?
There are things we know we know Easy Data
There are things we know we don’t know
There are things we don’t know we know
There are things we don’t know we don’t know
Thanks, Donald Rumsfeld and Lean Analytics

12. Why data?
There are things we know we know Easy Data
There are things we know we don’t know Easy Questions
There are things we don’t know we know
There are things we don’t know we don’t know
Thanks, Donald Rumsfeld and Lean Analytics

13. Why data?
There are things we know we know Easy Data
There are things we know we don’t know Easy Questions
There are things we don’t know we know
Valuable Intuitions, and
Difficult Questions
There are things we don’t know we don’t know
Thanks, Donald Rumsfeld and Lean Analytics

14. Why data?
There are things we know we know Easy Data
There are things we know we don’t know Easy Questions
There are things we don’t know we know
Valuable Intuitions, and
Difficult Questions
There are things we don’t know we don’t know Potential Opportunities
Thanks, Donald Rumsfeld and Lean Analytics

15. Why data?
Data validates what we know, underscores what
we don’t know, surfaces our intuitions and
exposes potential opportunities.

16. Types of data
• Quantitative and Objective
- Information collected in sufficient quantity that
significance can be determined numerically
• Qualitative and Subjective
- Information collected where significance is
determined experientially

17. Types of data
• Quantitative and Objective
- Measured by instrumenting an app with tracking
code, querying the database or log files
• Qualitative and Subjective
- Measured by interacting one-on-one with users

18. How we work with data
Design & BuildCollect & Analyze
Define goals &
Craft hypotheses

19. How we communicate data

20. Business Model
Canvas, Personas, User
Journeys, User
Interviews
Learn
Design & Build
Take steps based on
things we’ve learned.
Might start with a
design sprint.
Measure
Collect qualitative
reports from user
interviews.
Collect quantitative
data from app.
A likely iteration for stickies

21. Iteration Planning
1. Project Kick Off and Learning Activities. 1 week.
2. Design Sprint - design and build a low fidelity
version with one or more proposed features and
use to interview users. We might do more than
one of these. 1 week.
3. Product Development - incorporate what we
learned into the actual product and release for
real world feedback. 1 week.
4. Repeat #2 or #3 with new knowledge.

22. Questions?