This document discusses supporting qualitative data needs and summarizes discussions from groups on this topic. It was noted that while numbers typically carry more weight, some funders are starting to value qualitative data like stories and learning. Groups said they collect a lot of qualitative data but don't often analyze it systematically. The document outlines methods and tools used in a past qualitative data project, including interviews, thematic analysis, and journey mapping. Challenges collecting and analyzing qualitative data are discussed. Next steps proposed include considering achievable options for small groups and developing tools or training.

1. DISCOVER. LEARN. ANALYSE. SHAPE. REPEAT
Hard Talk
SUPPORTING QUALITATIVE DATA NEEDS

2. ✔Data Journeys Report
✔Qualitative data methods
✔Useful tools
✔Your experiences
✔Next steps
What we’re
talking about
this morning

3. Download the table version

4. Groups said
✔Data is typically synonymous with
numbers.
✔Numbers carry more weight with funders.
✔Funders are changing. Some value
qualitative data (stories, learning) more.
✔Stories used to back other insights when
improving practice
✔Collecting and using statistics / numbers
can perpetuate harm
✔Qualitative methods, such as peer research
and co-design can shift balance of power

5. Groups said
✔They collect a lot of qualitative data.
✔Many undertook their own projects.
✔Most don’t analyse this data methodically.
✔Tools are designed for academic use.
✔They are costly and not user friendly.

6. Research is so important,
especially when it comes
from us, because we are
service user-led and we
have lived experience.

7. Methods and
tools
Used in the Data Journeys research
and beyond

8. Interviews and thematic analysis
Methods
✔Initial discovery workshop
✔One to one interviews
✔Case study interviews
✔Journey mapping workshops
✔Thematic analysis
✔Sense checking findings
✔Visualising findings
Tools
✔Jamboard
✔Otter.ai
✔Google docs
✔Canva

9. Journey
mapping
example
Act on insights
CRM
Funding/ role
Sector data
Policies
Logistics
Data culture

10. Our reflections
✔It takes a long long time!
✔Canva did help synthesise analysis
✔Newer tools like Quirkos are relatively
cheap and easy to use (tested recently)
✔Digital coding could be helpful to give
weight to swamps/ challenges
✔And to organise / revisit insights
✔But Google Docs did work well enough -
relatively straightforward themes
✔Spreadsheets helped for more complex
coding (CRM challenges)

11. AI tools: GPT4
Are there opportunities for small
organisations to use AI?

12. AI tools: Ask CSV
Are there opportunities for small
organisations to use AI?

13. Your
experiences
Challenges, techniques and tools

14. How do you use your
qualitative data?
What tools do you use?
Are you using AI?

15. Challenges/ experiences
with qualitative data?

16. What could we do
collaboratively, in our
organisations or as
Datawise London?

17. What’s next?
✔Consider what’s achievable for
small groups
✔Tools – build if necessary
✔Training programme 2p 3

18. www.datawise.london
nissa@thinksocialtech.org
info@superhighways.org.uk
Thank you for
taking part