Community volunteer Tiffany Wang
“It requires deep listening, patience, and iterative collaboration to understand the context and history behind the data before you can identify the right approach.”
Pronouns She/Her
Roles Business Intelligence Engineer at Amazon Prime Video | Community Committee 2024 member, Data project volunteer
Links
Tiffany has been volunteering with DataKind UK since 2023. She started as a DataDive participant and quickly became a Data Ambassador, working directly with third sector partners. In 2024, she joined the Community Committee, supporting volunteer recruitment for events and running introductory sessions for newer volunteers. She's also represented DataKind UK at events like the Data Science Festival in 2024, and the Ealing AI Conference in 2025, sharing our work and passion for data for good with the wider community.
How did you get into data?
A few years ago, I was working in the real estate development industry in San Francisco - the heart of the tech industry. But my day-to-day still involved manually opening spreadsheets one by one to fill in numbers. This was during the COVID lockdown. Out of frustration with the limited technology I was working with I asked my housemates, who worked in tech, to teach me Python. Three months later, I started working as a data analyst at a startup in Singapore, and the rest is history.
What advice would you give to someone getting started?
There are tons of tools and methodologies out there, and it's easy to feel overwhelmed by how much you don't know. Pick a subject you already have some knowledge of and interest in, and play around with the data that you can find.
Kaggle is an excellent starting point. You can experiment with real data and learn from how others approach analysis. Shout out to my San Francisco housemate who worked at Kaggle and introduced me to the platform!
What was your first volunteering experience with DataKind UK like?
My first DataDive was with Material Focus, a national charity working to increase recycling of electrical items across the UK. Over two days, we split into different teams, each tackling specific questions about their data. It was fascinating to see how each group brought entirely different approaches and skill sets.
The collaborative energy was incredible, and seeing Material Focus implement the findings really showed me the impact this kind of volunteer work can have. I later had the opportunity to represent DataKind UK at the Data Science Festival in 2024 to share our data for good approach with the broader community, using that project as example.
What have you learned working on data for the third sector?
Two critical lessons stand out. First, safety and privacy are paramount, especially with third sector data, which often contains demographic information that can reveal personal identities.
Second, working with third sector organisations is fundamentally different from the private sector. You're often working with limited resources, basic tech stacks, and partners who may have little data expertise. Sometimes the data set is scattered spreadsheet files with merged columns. It requires deep listening, patience, and iterative collaboration to understand the context and history behind their data before you can identify the right approach.
Is there a resource you'd recommend?
Kaggle is excellent for hands-on learning as I mentioned. You can find interesting datasets, run analyses entirely in the cloud, and see how other data professionals approach problems.
For building strong statistical foundations, I recommend StatQuest. The creator breaks down complex concepts into digestible explanations, and I genuinely enjoy that each episode has its own theme song!
