This document discusses data literacy from a product management perspective. It defines data literacy as making sense of presented data, using it to find answers while understanding its limitations. It also discusses three common struggles with data: dealing with vague data requests, implementing self-service data tools, and improving poor data standards like free-text fields, lack of unique identifiers, and large unorganized tables. The document provides recommendations for addressing these struggles, such as getting input from data experts and not compromising on quality. It emphasizes being relentless but also patient in improving data literacy and standards, and getting others invested in the importance of high-quality data.

1. “Please don’t
shoot the PM”
1
or “Data literacy from a product
management point of view”
in/clairedevilard @claireandcleartabsoft.co/2qr91Dn

2. How to
define data
literacy.
2

3. 33
Data literacy is at the crossroad of
statistical literacy, information literacy
and technical skills.
Based of the definition from the
UN Data Revolution Program

4. 44
One who makes sense of the data
presented to them, use it to get
answers, but also know its limits.
What does “ being data literate” mean?

5. Why the
hell am I
here.
5

6. Let’s talk
about
struggles.
Struggle 1
Data request
nonsense
6
Struggle 2
Implement
self service
Struggle 3
Improve data
standards

7. Struggle 1,
data
request
nonsense.
7

8. 8
“Find me some
good stats!”

9. 9
“Just find me
some stats!”

10. Say no. Ask why. Work
together on something else.
How do deal with data request nonsense
10

11. Struggle 2,
implement
data self
service.
11

12. Actually
becoming
“data-led”.
Credits: Jaime Lees for The Riverfront Times, July 2018
12

13. Filterable
tables can
be enough.
Credits: JobLadder. All figures shown are test data.
13

14. There’s
never
enough
usability
notes.
Credits: JobLadder. All figures shown are test data.

15. Struggle 3,
improve
data
standards.
15

16. Data hell 1,
free text
fields.
Credits: JobLadder. All figures shown are test data.
16

17. Data hell 2,
tracking by
name and
not by ID.
Credits: JobLadder. All figures shown are test data.
17

18. Data hell 3,
oversized,
unkempt
tables.
Credits: Giphy. Link: gph.is/1O8HH5H
*CRIES IN SQL*
18

19. 19
1. Get the “data person” in the room ASAP.
2. Don’t settle if you know better.
When you set up NEW data standards

20. 20
When you clean up OLD data standards
1. Find the right angle for each stakeholder.
2. Get ready to compromise & give back.

21. A few more
lessons
before I
shush.
LESSON 1
Data literacy
is “person
relative”.
21
LESSON 2
Always be
relentless, yet
patient.
LESSON 3
Get people to
care as much
as you do.

22. LESSON 2
Always be
relentless,
yet patient.

23. LESSON 3
Get people
to care as
much as
you do.
23

24. Resources
that are
way better
than this
talk.
Scholarly resources
� “Data visualization literacy: Definitions, conceptual frameworks, exercises,
and assessments” by Katy Börnera, Andreas Buecklea and Michael Gindaa
� “Data infrastructure literacy” by Jonathan Gray, Carolin Gerlitz and Liliana
Bounegru
� “Toward an Understanding of Data Literacy”, by Hammad R. Khan,
Jeonghyun Kim and Hsia-Ching Chang
� “Why Companies Must Close The Data Literacy Divide” by Brent Dykes for
Forbes magazine
� “Your Data Literacy Depends on Understanding the Types of Data and How
They’re Captured”, by Hugo Bowne-Anderson for the Harvard Business
Review
� “New tools to improve statistical literacy”, Louise Corselli-Nordblad and Britta
Gauckler for the OECD
� “Data literacy and storytelling at the UN World Data Forum”, by Lisa Cornish
for DEVEX
Graphic template
Free “Juliet” Google Slides theme with brush stroke
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25. 25
Thanks for
listening!
in/clairedevilard @claireandcleartabsoft.co/2qr91Dn