As an investor in data-driven companies, I’ve been thinking a lot about my grandfather—a baker, a small business owner, and, I now realize, a pioneering data scientist. Without much more than pencil, paper, and extraordinarily deep knowledge of his customers in Washington Heights, Manhattan, he bought, sold, and managed inventory while also managing risk. His community was poor, but his business prospered.

This was not because of what we celebrate today as the power and predictive promise of big data, but rather because of what I call small data: nuanced market insights that come through regular and trusted interactions.

Big data takes into account volumes of information from largely electronic sources—such as credit cards, pay stubs, test scores—and segments people into groups. As a result, people participating in the formalized economy benefit from big data. But people who are paid in cash and have no recognized accolades, such as higher education, are left out.

Small data captures those insights to address this market failure. My grandfather, for example, had critical customer information he carefully gathered over the years: who could pay now, who needed a few days more, and which tabs to close.

If he had access to a big data algorithm, it likely would have told him all his clients were unlikely to repay him, based on the fact that they were low income (vs. high income) and low education level (vs. college degree).

Today, I worry that in our enthusiasm for big data and aggregated predictions, we often lose the critical insights we can gain from small data, because we don’t collect it. In the process, we are missing vital opportunities to both make money and create economic empowerment.

Read the source article on small data by Liz Luckett at Stanford Social Innovation Review