Removing Data Silos with a Bottom-up Approach

The paytech provider struggled with slow decision-making and no unified view of customer interactions.

The company aimed to sharpen its operations with actionable insights by deploying data and AI platforms. Getting there meant working through:

• fragmented data from diverse sources
• hidden legacy dependencies
• siloed systems across the organization

To enable data-driven insights and pave the way for future AI capabilities, the paytech provider needed to transform both its data and its governance. Internal politics meant a large-scale program was not an option, so the work called for an out-of-the-box approach.

Darize delivered a self-service platform with minimal additional resources, transforming the client’s data and governance while scaling across the organization with a bottom-up approach.

• We evaluated systems and processes to understand operational needs and assess the changes required
• We started by building working prototypes for one to two teams, delivering business analytics functionality within months
• We developed a self-service platform with a data mesh approach and a modern data stack (Databricks + dbt)
• We unified and integrated data from Hubspot, SAP, and Zendesk into the self-service platform
• We built internal support through proven business value, scaling incrementally as demand grew organically
• We onboarded teams through training sessions, building capabilities and ownership to establish data governance

With a focus on business value, the platform gave the client its first ever 360° customer view, empowering teams with real-time insights through self-service tools and delivering impact across the organization:

• Increased operational efficiency and reduced manual reporting by 70%
• Uncovered customer behavior patterns through cross-system insights
• Enabled proactive identification of opportunities through real-time insights
• Enhanced sales targeting and customer success strategies
• Improved resource allocation based on integrated business metrics
• Supported AI initiatives by building a structured, high-quality data foundation
• Drove scalability through replicable patterns and internal advocates
• Shifted culture from data skepticism to advocacy