Rebuilding a fragmented data & analytics function to accelerate decisions and support 77% revenue growth.

The situation

A fast-growing cloud services provider had outgrown the way it made decisions. Leaders across 8 functions had built their own analytics teams to work around a central function they saw as slow, and business definitions varied depending on who you asked.

Leadership got conflicting answers to the same question, had no view of what the fragmented analytics landscape cost, and described operating as flying blind. Business reviews ended with follow-up analysis requests instead of decisions, and a new business question took weeks to answer – all while leadership pushed for higher profitability without adding headcount.

Business leaders felt the “data needs to be fixed,” while the data team said it needed more capacity to keep up. In reality, two problems reinforced each other: the data architecture was built around systems instead of how the business operated, and siloed analytics resulted in redundant work that further slowed execution.

The solve

Functional leaders co-designed the strategy instead of receiving a central redesign, so the leaders who had built their own analytics teams shaped the new model rather than resisting it.

A hub-and-spoke model – chosen over fully centralized and dotted-line alternatives – paired a center of excellence for specialized skills with analysts embedded in each function, giving leadership a way to shift analytical capacity to the highest-value problems as priorities changed. Standardized roles and an interaction model defined what embedded analysts and the center of excellence each owned, with guardrails in place to manage quality and risk.

A redesigned operating rhythm turned ops syncs, MBRs, and QBRs from data readouts into decision forums, with actionable insights prepared before each meeting.

Underneath, a unified data foundation – designed around how the business operates – gave every function a single source of truth, and embedded analytics teams could now focus on business analysis instead of data wrangling.

The outcome

New business questions were answered in hours instead of weeks – a 98% reduction. Business reviews that once ended in follow-up analysis requests started ending in action plans with owners and deadlines.

Decisions that had been impossible, or previously took weeks of ad-hoc analysis, became routine: funnel leakage traced to its source, performance risks caught early enough to reallocate resources, and channel mix optimized on full-funnel attribution.

Faster decisions and faster execution let the organization grow revenue 77% over two years with flat headcount, as analytics capacity shifted from data wrangling to the highest-value problems.