Editor’s note: Kuang Chen is the CEO and founder of Captricity.
The old saw about effective management goes, “You get what you measure.” Today, whether you call it growth hacking or business intelligence, successful large corporations and lean young startups have become fanatical about measuring their customers. The leaders of the pack – like Uber (which collects data on the supply and demand of their riders and drivers) and Zenefits (which manages data on all aspects of HR) – know that data about their customers is the lifeblood of their explosive growth.
Compared to companies like Google, which tested out a data-based promotion system, important nonprofits and civil society organizations that are also tackling real-world issues don’t have the same timely, fluent access to their data. Just look at Veterans Affairs or any health clinic in the developing world.
Why should this high level of fluency in customer data be reserved for the rich and high tech? What if nonprofits (really, the organizations that serve everyone) had the same data fluency as the leading firms that only serve people with expensive smartphones or 401K plans?
Having helped dozens of nonprofits get to near-real-time access to their customer data, I’ve seen firsthand how much data access can help.
Sanergy is one of the nonprofits I work with to help turn its paper data into structured, digital data. A social enterprise that brings sanitation to the areas that most need it, they started out building toilets in the Mukuru slum of Nairobi and have rapidly expanded from there.
When the people sent to service the toilets were repeatedly targeted for thefts because of the tablet computers they carried, Sanergy switched their reports to paper. And they began to use Captricity’s data-as-a-service technology to enter their servicing reports to help monitor usage and service to ensure the toilets continue to serve the people for whom they were built.
By focusing on data access, Sanergy has achieved similar levels of quantitative fluency to a lean startup or large corporation while working in some of the most challenging conditions in the world.
It was the work of the physician and anthropologist Paul Farmer that initially inspired me to pursue this path of leveraging data to serve those most in need. When he started working on treating multi-drug-resistant (MDR) tuberculosis, he refused to listen to established thinking, which said it would be impossible to achieve the level of adherence necessary when trying to treat people in remote and under-resourced settings. (The drug regimen for tuberculosis is particularly grueling, and lack of adherence can lead to even more drug resistance to this already virulent and hard-to-treat disease.)
Farmer’s solution was to form networks of community health workers (CHWs) who would visit each patient every day to watch them take their medication and record it in writing, so that patients’ adherence could be easily tracked. The results were stunning – showing higher adherence among TB patients in Haiti than in the United States and many other developed countries.
This simple step of using community-sourced workers to visit and monitor patients in their homes has become one of the greatest healthcare innovations of our time.
Having data made all the difference in tackling TB specifically. More important, Farmer proved that this model of care could be used to effectively treat other diseases requiring complex drug regimens (like HIV) in the poorest places in the world. His CHW programs were not easy, mostly using paper forms that are meticulously typed into (sometimes solar-powered) databases.
The more we help organizations of all kinds unlock the data in their everyday operations – from the largest corporations to the most remote non-profits in the developing world – the greater our chances of solving the most urgent problems of our time.
Will data-crunching technologists one day replace doctors? Absolutely not. But can we as technologists rally to their side and magnify their effectiveness ten-fold? It’s worth trying.