Challenge

Rimini Street provides enterprise software support and services to some of the world's largest organizations; it is a business that depends on operational precision and data accuracy across a complex global customer base. Behind that precision was a data operation that had not kept pace with the business. Years of one-off projects had created significant tech debt, layering complexity onto the simplest tasks until even routine data work required specialist knowledge to execute correctly. The team of 23 data analysts who owned that operation was not doing data strategy. They were doing data support. The majority of their time was absorbed by ad-hoc requests from across the business — list loading for campaigns, list building for sales, chasing down data errors and inconsistencies that had accumulated across systems with no unified governance. Every request required analyst involvement because there was no self-service alternative. The volume of those requests was not declining. And the business's expectation that data would be available quickly, accurately, and at scale was not going away. Without a structural change, the only answer was more headcount, which was neither efficient nor sustainable.

Solution

Rimini Street deployed Openprise with a clear objective: give the rest of the business the ability to handle its own data requests without pulling an analyst into every task. Openprise App Factory was the mechanism. Using App Factory, the Ops team built self-service apps tailored to the most common request types — list loading, list building, and data cleanup — and deployed them to the teams who had been generating the ad-hoc ticket volume. Those teams could now execute their own requests through a structured, automated interface without needing to know how the underlying data logic worked. The analyst team's role shifted from executing requests to building and maintaining the apps that made self-service possible.

Alongside the App Factory deployment, list loading, cleaning, and enrichment processes were automated end-to-end — eliminating the manual steps that had been consuming analyst hours across every campaign and event cycle. Multiple point solutions that had been handling different parts of the data operations were replaced with Openprise, consolidating the stack and removing the integration overhead that had added to tech debt. The structural outcome was equally significant: with analyst time freed and a platform in place that provided visibility and control over data operations, Rimini Street formed a Data Governance Committee to oversee and plan future data projects. That committee was not possible before Openprise. The team had not had the capacity to think about governance — they had been too busy fielding tickets.

Without Openprise, we would need to hire ~15 additional FTEs to manually do the work.

— Detrie Zacharias, Director, Global Operations

Impact

The headline number is 108+ hours per week saved in human resources — but that figure understates the actual change. This represented a wholesale shift in how the data analyst team operates. Work that had previously required analyst involvement — every list load, every error chase, every ad-hoc data request — was either automated or routed through a self-service app. The team's output did not decrease. Their capacity for strategic work increased.

The 40 Ops tickets reduced weekly is the measurement that tells you where the time went. Each of those tickets was a request that had previously landed with an analyst and required context gathering, execution, and handoff. The self-service apps Rimini Street built in Openprise App Factory absorbed that volume — handling the request automatically, applying consistent logic, and returning the output without analyst intervention. For the teams generating those requests, the experience improved too: they got results faster and without depending on an analyst's availability.

The ~15 FTE calculation is the most direct statement of what the automation is worth in headcount terms. Without Openprise, Rimini Street would have needed to grow the data team by approximately 15 people to handle the same volume of work manually. Instead, they did not add headcount. They added capability. And with a Data Governance Committee now in place, the organization has the structure to make deliberate decisions about what to build next — rather than simply reacting to the next wave of ad-hoc requests.