1. THE MATCHBOX
Where Transparency and Accountability Advocates
Get Project Support

2. WHAT IS THE ENGINE
ROOM?
We investigate and support
the effective use of data
and technology in advocacy
through:
● Applied research
● Direct support

3. WHAT IS THE MATCHBOX?
An accelerator for NGOs
Strategic and material support
Matchmaking between projects and experts

4. IS IT FREE?
It depends.
We want to partner with great organisations.
If you can pay, we charge on a sliding scale.
If you can’t pay, we fundraise from donors.
It is also very selective to ensure we deliver.

5. WHO IS IT FOR?
Advocacy organizations
Transparency and accountability projects integrating
technology into their work
Latin America and Southern Africa (for now...)

6. ...SO WHAT EXACTLY DO
WE DO?
Idea refinement
Project planning
Matchmaking
External expertise

7. GIVE ME SOME EXAMPLES
Mexico: crowdsourcing public spending
Argentina: tracking bills in parliament
Zimbabwe: water monitoring with WhatsApp
Namibia: visualizing petroleum exploration

8. WHAT WE LEARNED
we’ve noticed a lot of patterns in how civil society,
journalists, and advocates can break complex ideas into
workable projects

9. TECH. HUH. WHAT IS IT
GOOD FOR?
Do you really need tech?
What is tech? (Baby don’t hurt me…)
Useful beats shiny every time
Avoid complexity

10. THINK SYSTEMIC
Technology can’t be compartmentalized
into a single project
Tech culture - learn the basics
In-house technical capacity
Make it last

11. ONE STEP AT A TIME
Taking technology one-step at a time is important to
develop smartly
Rushing into a technology overhaul will result in missteps
and poor planning

12. PREP, PREP, PREP.
Prep.
Many problems can be avoided by taking enough time to
think about a project before implementing it.

13. GO LOCAL
Don’t overly rely on international funders and technology
support providers (including us!)
Become familiar with technology communities in your city
or country.

14. THINK LIKE A HUMAN
User experience is key.
It doesn’t matter what you’re building - you are creating an
interaction.
Invest time to make sure your audience will want to engage
with your project.

15. ...THEN THINK LIKE A
MACHINE
What will your data model look like?
What’s a data model?

16. QUESTIONS TO ASK
Key questions that you should ask yourself while
designing a project. These questions can save you
loads of time and help you refine your idea into a
workable project plan.

17. WHO IS THIS FOR?
Obvious, but again and again we see projects that do not
explore their real audience.
If you are building a tool for the ‘general public’ then you
haven’t done your homework.
The more time you spend unpacking what communities
your projects are for, the better your project will be.

18. WHAT DATA WILL I NEED?
Data is the petrol for technology projects.
Understanding what data you will need to get a project
moving is key.
Scraping? Crowdsourcing? SMS? Better know that at the
outset and plan for it.

19. IS IT SAFE?
Data is information - usually about human beings.
Protect the privacy of your community.
Controversial themes can make you a target.
Have doubts? Ask for expert advice.

20. AM I REINVENTING THE
WHEEL?
Are you the only one who tried this?
Hopefully there is someone somewhere who has built
something similar to what you are thinking about.
Comparable research and connections with similar projects
can inspire and provide you with headstart thinking and
resources.

21. WHAT OPEN TECH CAN I
USE?
Only start from scratch as a last resort.
Transparency and open source go hand in hand.
There are likely a lot of open source projects that can
jumpstart your project.
Meet new people, help a friend.

22. OUR PROCESS
Every project is different, but there are certain steps that
will make any project stronger, more focused and
remove some uncertainties.

23. 1.CHECKING
ASSUMPTIONS
Hypothesis of change
Brainstorm your assumptions internally
Check them with your audience

24. 2. DISCOVERY PHASE
Has anyone else done this?
Is there anything similar I can learn from?
What were their biggest wins?
What were their biggest obstacles?
What tools, resources, communities are out there that can
support me?

25. 3. MAKE A PLAN, STAN
Start with a pilot
Design a broad project plan
Figure out what expertise you will need along the way
Find the expertise
Refine project plan

26. 4. SKETCH IT OUT
Wireframes
Audience is crucial
Avoid the echo chamber

27. 5. A HOUSE FOR YOUR
DATA
Develop the data model
Build your database
Put actual data in

28. 6. A FACE FOR YOUR
PLACE
From wireframes to interface
Visual design is your friend
Keep it simple

29. 7. BUILD AND REBUILD
Test early and often
Watch it break horribly
Rinse and repeat
Share with your audience

30. 1. CHECKING ASSUMPTIONS
2. DISCOVERY PHASE
3. MAKE A PLAN, STAN
4. SKETCH IT OUT
5. A HOUSE FOR YOUR DATA
6. A FACE FOR YOUR PLACE
7. BUILD AND REBUILD

31. GET IN TOUCH!
thematchbox.io
tiny.cc/matchboxpoint15
@engnroom
@tingeber