Removing Data Silos with a Bottom-up Approach

How 7N delivered bottom-up data mesh implementation to remove data silos for a European paytech client

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CHALLENGE

Insights limited by fragmented data


A leading European provider of innovative digital payment solutions for individuals, businesses, and financial institutions was struggling with slow decision-making and no unified view of customer interactions.

Aiming to enhance business operations with actionable insights by deploying data and AI platforms, they faced challenges caused by:

  • 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, they needed to transform both their data and its governance. However, internal politics meant that a large-scale program was not an option, requiring an out-of-the-box approach.

SOLUTION

Enabling self-service insights

7N 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.

Evaluated systems and processes to understand operational needs and assess necessary changes

Started by creating working prototypes for 1-2 teams to deliver business analytics functionalities within months

Developed self-service platform with a data mesh approach, and modern data stack (Databricks + dbt)

Unified and integrated data from Hubspot, SAP, and Zendesk into self-service platform

Built internal support through proven business value, scaling incrementally through organic demand

Onboarded teams via training sessions, building capabilities and ownership to establish data governance

IMPACT

From data chaos to business impact

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

Increased operational efficiency and reduced manual reporting by 70%
Discovery of 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 scepticism to advocacy

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