Article by Taylor Butler: “A sales receipt, a web search, a satellite image, a job posting that expired two years ago; the seemingly mundane multitudes of data being created by daily activities hold unique and valuable insights for researchers to parse.
What is alternative data, and what makes it so useful for research? We’re not talking about fringe science here, but the creative application of non-traditional sources of data to answering research questions. I spend a lot of time working with a catalog of datasets that captures a vast and varied array of human behavior and world phenomena, and I am constantly inspired by the innovative problem solving researchers employ to extract answers from new sources and diverse perspectives.
What makes data “alternative”?
Alternative data, with its many alternate names (organic [data], novel, found, digital trace, unstructured, observational, derived, etc.), is essentially any data that was generated for some purpose other than research – typically as a byproduct of some administrative, commercial, or technical process – and then utilized for research. Common sources of “alt data” include satellite imagery, mobile-phone records, usage data, administrative records, transaction records, job postings, social media and internet activity, sensor data, and much more.
This differs from traditional datasets that are designed, built, generated or measured, such as structured surveys, experimental data collection, or official government reporting. Alt data offers novel “proxy” measurements of real-time, real-life behavior and phenomena, giving researchers unique perspectives to new and old questions. While economics has paved the way in alt data usage, adopting large-scale administrative and private sector data in the study of market trends (Einav & Levin, 2014), many fields are also implementing new data streams into their research, from global health to climate change, public policy and more.
Take an age old question like: does spending time in nature improve mental health? There are many ways researchers go about answering this, the most traditional method typically being a survey or maybe even a random assignment experiment. But these researchers (Chandra Mouli et al., 2026) combined a traditional data source (CDC health measures) with real-life measures of behavior (cell phone mobility data) and neighborhood green space (NASA satellite imagery). Looking at nine US metro areas, they found that neighborhoods where residents actually visited nearby green space had lower rates of poor mental health; the presence of parks alone did not explain it. No single one of these three sources could have provided this nuanced insight; the combination of disparate data sources provides researchers with opportunities to explore old questions in creative new ways…(More)”.
