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.
We won’t solve this problem of big data by returning to my grandfather’s shop floor. What we need is more and better data—a small data movement to supply vital missing links in marketplaces and supply chains the world over. What are the proxies that allow large companies to discern whom among the low income are good customers in the absence of a shopkeeper? At The Social Entrepreneurs’ Fund (TSEF), we are profitably investing in a new breed of data company: enterprises that are intentionally and responsibly serving low-income communities, and generating new and unique insights about the behavior of individuals in the process. The value of the small data they collect is becoming increasingly useful to other partners, including corporations who are willing to pay for it. It is a kind of dual market opportunity that for the first time makes it economically advantageous for these companies to reach the poor. We are betting on small data to transform opportunities and quality of life for the underserved, tap into markets that were once seen as too risky or too costly to reach, and earn significant returns for investors.
Consider basic credit scores. Because banks need a cost-effective way to assess repayment risk, they have come to rely on credit scores to decide whether to loan in the first place. Traditionally, credit scores require a data trail built on income pay stubs and up to a decade of debt repayment information. People with erratic, cash-based employment lack this data, and are often plagued for years by late or missed payments. They can’t demonstrate their credit worthiness by conventional measures and consequently pay more for loans. The wealthy are a good risk, but many people who need credit to improve their circumstances—to finance a home, a car, their child’s education, a small business—struggle to get it.
Credit scores, of course, are not the only way to determine likelihood of repayment, but they are often the most cost-effective, if inaccurate, proxy. In the absence of nuanced, small data, we have mispriced risk for hundreds of millions of people across the globe, often because the cost of accessing that risk is too high. We are denying people affordable access to capital and denying lenders profitable opportunities. What if, instead, we had small, better, more accurate, and therefore smarter data?
One of TSEF’s investments is in Angaza, a mobile technology platform that allows poor people in developing countries to pay for alternative energies like solar power in small installments, rather than all costs up front. By some estimates, pay-as-you-go companies like Angaza have accelerated solar adoption by five fold, selling affordable and clean power to people off the electrical grid who would otherwise depend solely on inconsistent access to expensive and noxious energy sources like kerosene.
Yet Angaza’s mobile technology not only reduces “energy poverty,” but also allows families to establish first-time credit histories by documenting their payments and repayment schedules in markets where credit bureaus provide only negative information. As Angaza accumulates this data, it can observe patterns: who pays and when, by geography, season, or community. It can analyze whether there are cycles to income, payment, or missed payments? Accordingly, it can help distributors of solar make better risk assessments and business decisions—for example, whether to repossess a solar panel (costly for all involved) or extend a payment schedule. With this repayment data, families can move up the credit ladder to finance a broad range of needs. Those needs in turn represent market opportunities for other businesses, including energy or utility companies, or local banks with products and services they can sell profitably, responsibly, and cost-effectively into these untapped markets.
The small data revolution goes well beyond the consumer. It is also changing the way small businesses operate and thrive. Take the case of Frogtek, a company in the TSEF portfolio that helps small shop keepers in Mexico manage their inventory. Imagine my grandfather’s bakery as a Mexican tienda, or store. Frogtek sells a point-of-sale tablet and scanner that collects data on all the products shopkeepers stock in small stores and allows the shopkeepers—who have traditionally relied on cash-based paper records—to track what people buy. The resulting data generates valuable information about customer purchases and preferences. This not only improves inventory management, but also allows shop owners to generate a profit-and-loss statement. Thus, for the first time, they can demonstrate transaction, payment, and repayment data, which in turn could qualify them for loans and capital to expand their businesses and serve their customers better.
This kind of data can also improve efficiency along the supply chain. Today, 50 percent of food in Mexico is sold through small shops with cash transactions. As a result, large consumer packaged-goods companies lack insight about basic consumer behavior—what people buy, when, at what price—that would allow them to streamline delivery. Frogtek’s small—but new and improved—data allows for massive improvements in efficiency (and cost savings) along murky supply chains, opening up potential for markets to be more profitable and appealing.
In our experience, large companies are willing to pay for this data. One of the world’s largest information companies, for example, invested in Frogtek to provide its customers with insight it couldn’t capture on their own. And it is precisely the economic value of these insights that is turning traditional barriers on their heads and into profitable opportunities. It is now possible to pass along these costs of data collection to consumer goods companies, utility companies, insurers, banks—any enterprise that sees better data and risk assessment as a pathway to new markets. This insight is as true in developing-country markets as it is in our communities here at home.
By investing in companies that generate small data—and big insights—we believe it is possible to profitably and responsibly serve low-income communities across the world.