At Rising Star Outreach, data tools are helping staff in India’s leprosy colonies move from scattered records to earlier intervention, stronger funding visibility and more coordinated care.
A nonprofit working in leprosy colonies in India is not the typical setting for enterprise data infrastructure.
That is part of what makes the shift underway at Rising Star Outreach (RSO) notable.
The organization supports more than 100 leprosy colonies in India with medical clinics, schools, and micro-grants to help people affected by leprosy start small businesses. RSO is using dashboards, alerts and benchmarking tools more commonly associated with corporate operations teams to track student performance, compare outcomes across regions and improve program delivery.
The objective is not efficiency in the traditional corporate sense. Staff say the systems are helping identify problems earlier and improve coordination across programs serving communities where stigma still shapes daily life.
Leprosy remains among the world’s most stigmatized diseases with more than 200,000 new cases reported each year according to the World Health Organization. Even where treatment is available, people affected by it are often excluded from schools, jobs and broader social systems. Addressing those barriers requires coordination across education, healthcare and community support systems that do not always operate together.
For years, RSO managed information across multiple programs and teams. Data existed across spreadsheets, local records, paper files and individual staff knowledge. The issue was less about collection than visibility.
Staff often lacked a unified view of what was happening across programs or regions.
Like many organizations managing complex services, RSO had information, but not always systems that allowed it to be used consistently or in real time.
RSO’s staff was later introduced to Domo through the company’s Domo for Good program, which provides software support to selected nonprofits working through operational and data challenges.
With support from Domo employees and volunteers, the organization began consolidating information into centralized systems.
The process took time.
Teachers in RSO schools now receive alerts when a student’s performance declines. Interventions are logged and tracked, allowing staff to identify which actions lead to improvement and which students may otherwise go unnoticed.
Staff say the shift has helped move some decisions from reactive responses to earlier intervention.
“In our work, the value of data is not theoretical,” Brett Caywood, President at RSO told me over email, “When a teacher can see a student slipping earlier, when a program manager can compare what is working across colonies, or when a sponsor issue can be resolved before it affects funding, data becomes part of the care system. The goal is not to turn nonprofit work into corporate operations. It is to give the people closest to the mission better visibility so they can act sooner and serve more effectively.”
The same systems also allow the organization to compare outcomes across schools in the leprosy colonies where RSO has operations. Geographic benchmarking has long been common in enterprise environments, where companies evaluate performance across operations, markets and supply chains. Its use in nonprofit settings remains less common.
The software resembles tools used in corporate operations, but the intended outcomes are different.
In business settings, data platforms are often used to optimize revenue, forecast demand and manage operational risk. In nonprofit settings, organizations are increasingly using them to track human outcomes, including whether students remain in school, whether interventions are effective and whether programs reduce vulnerability over time.
The shift is less about the technology itself than how organizations incorporate information into day-to-day operations.
Historically, many nonprofits have used data primarily for reporting, particularly for donor, grant and fundraising requirements. Increasingly, organizations are also trying to use it while programs are actively operating.
That requires more than software.
Organizations need connected systems, consistent processes and staff trained to use them during daily work. Adoption also remains uneven. Budget constraints, legacy systems and competing operational priorities can slow implementation.
Integrating information into existing workflows can also take time. Staff must learn new systems while continuing to deliver services. Confidence in the data often develops gradually.
At RSO, staff members are not data analysts. They are teachers, healthcare workers and program managers.
Making data useful means incorporating it into routines rather than adding another administrative layer.
Some of the earliest applications have been practical. The organization uses automation to identify expired payment methods in its sponsorship program, helping reduce administrative gaps and stabilize funding flows.
These are relatively small operational changes, but staff say they have improved reliability over time.
More advanced uses, including artificial intelligence, are also beginning to emerge. With more consistent information, organizations can begin identifying risks earlier, prioritizing interventions and understanding how different programs interact.
“There is a broader lesson in Rising Star’s work: AI can only be useful when the underlying data is connected, trusted and tied to real workflows,” said Mark Boothe, chief marketing officer at Domo. “That is true in a global enterprise, and it is true for a nonprofit operating in leprosy colonies in India. The promise is not AI for its own sake. The promise is helping teams see what is changing, decide what needs attention and respond faster.”
RSO has worked with forward-deployed engineers at Domo to develop AI-powered applications and agents intended to help staff respond more quickly to emerging issues.
But the underlying requirement has remained the same.
Data must be accurate, connected and trusted.
Without that foundation, advanced tools have limited value. Systems built on incomplete or inconsistent information can create additional inefficiencies rather than reduce them.
In many respects, the work resembles infrastructure modernization more than digital transformation. Much of it involves connecting systems, improving data quality, training staff and integrating tools into existing workflows.
The changes are incremental and often difficult to see in real time.
Taken together, however, they reflect a broader shift in how some nonprofits are beginning to operate.
The same principles that guide enterprise systems, including visibility, coordination and measurement, are increasingly being applied to social and healthcare challenges.
For Domo, the work with RSO reflects a broader effort to make enterprise software available to organizations operating outside large corporate budgets.
In this case, systems originally designed for business operations are being adapted into tools used across education, healthcare and colony development programs.
The broader change may be less about access to technology than the operational systems needed to make that technology useful.
