Insights

A look into the hidden complexity of data pipelines, and some suggestions to improve the process.

How good are AI agents at data science? Here's what we've learned from initial experiments about what works, what doesn't, and what the future might hold.

CyFi enhances modern HAB monitoring programs by extending their reach and informing field-based components.

Using "baked" data to transform public data repositories into analysis-ready resources

What happens to winning solutions after a machine learning competition?

Reflections on how to build data and AI infrastructure in the social sector that serves the needs of nonprofits and their beneficiaries.

Data, evaltuation, product iteration, and public goods: reflections on the ASU+GSV Summit 2025.

This year DrivenData celebrates our 10th birthday! We've spent the past decade working to use data science and AI for social good. Here are some lessons we've learned along the way.

When non-profits when they shut down, we should pay attention to the assets they produce as public goods and how they can be used to drive impact.

What the shakeup in OpenAI's leadership tells us for the future of AI governance and the role of nonprofits in tech.

How can we use data and AI to help combat the effects of climate change? This post walks through real-world examples from DrivenData challenges working with public, private, and social sector organizations.

An initial guide to using LLMs productively as a data scientist. This post covers LLM APIs, prompting, use cases, and more.

This era of generative AI is changing how we think and talk about AI ethics, but we shouldn't lose sight of what matters.

Learn what wins data competitions in 2022 and 2023, and how you can apply these insights to your own work.

Classification algorithms give us a lower bound on how well we can distinguish categories; maybe machine learning competitions give us a way to estimate an upper bound.

Classification algorithms give us a lower bound on how well we can distinguish categories; maybe machine learning competitions give us a way to estimate an upper bound.

Over the years, we have gradually settled on a process for changing and releasing software that is robust enough to prevent common failure modes but lightweight enough to be minimally annoying. Here’s what it looks like when we make a change.

When social sector organizations think about data, the conversation often begins and ends with measuring impact. Here are some ways that social impact organizations can move beyond thinking about measuring impact and start using machine learning to transform how they operate.

The 2016 election was a bad showcase for the powers of data science.

Video footage of Peter and Isaac from their pilgrimage to SciPy 2016