Building a scalable data culture without rebuilding the stack
Objective
La Fosse sought to make data accessible across all teams, empowering them to make informed, data-driven decisions without technical barriers.
Obstacle
Fragmented systems and specialised recruitment tools created integration challenges, limiting visibility and slowing decision-making.
Outcome
A structured five-year data roadmap streamlined La Fosse’s data strategy, enabling scalable growth and future-proofed analytics.
Background
La Fosse is a leading recruitment firm specialising in data, IT, and technology talent. Built on a foundation of care and expertise, the company delivers tailored recruitment solutions across the UK and internationally, placing top professionals in both permanent and contract roles.
Although La Fosse already had an established data setup and a small internal data team, achieving true data democratisation remained a challenge. Their technology stack was complex, and data was not yet fully integrated across systems, hindering accessibility and collaboration between departments. To overcome these challenges and create a clear long-term vision, La Fosse partnered with 173tech to design a structured, five-year roadmap.
Challenges
Democratisation: While La Fosse’s data team had laid solid foundations, access to data across the organisation remained uneven. With multiple platforms and tools in play, integration gaps meant teams often struggled to find, interpret, or trust the data they needed. This created inefficiencies and limited the adoption of data-driven decision-making beyond the core data team.
Self-Service: To enhance efficiency, La Fosse aimed to implement a self-serve data model, empowering different departments to access the insights they needed without waiting for technical support. However, this presented a new challenge: each department had distinct requirements and varying levels of data proficiency. Some teams needed access to high-level dashboards for tracking recruitment performance, while others required automated reporting pipelines to monitor financial metrics or operational efficiency. Establishing a self-serve framework meant defining realistic timelines and prioritising which departments would be onboarded first. It was essential to create a structured roadmap to ensure that implementation was both achievable and aligned with business priorities.
Solution
No Large Migration: The leadership team at La Fosse was keen to future-proof their data infrastructure, ensuring long-term scalability and adaptability. However, they wanted to avoid large-scale migrations that could be time-consuming, resource-intensive, and disruptive to daily operations. Instead, they sought a strategy that would allow them to unlock the most value from their existing systems while making incremental improvements. This meant identifying areas where optimisation could be achieved without requiring a complete overhaul of their tech stack.
Integrating dbt: During our evaluation of La Fosse’s existing setup, we identified a significant limitation within Microsoft Fabric, specifically with Azure Data Factory. While Azure Data Factory provided a user-friendly, drag-and-drop interface, it proved insufficient for handling complex data modelling requests. This limitation placed a cap on what could be achieved in terms of reporting and analytics. Furthermore, a key issue was that only one member of the data team had expertise in using Azure Data Factory, while the rest of the team primarily worked with SQL. This knowledge gap created a dependency on a single individual, increasing risk and limiting efficiency. To address this, we explored the potential of replacing Azure Data Factory with dbt specifically for modelling.
Impact
Iterative and Collaborative Execution: We collaborated closely with La Fosse’s internal teams to understand their workflows, tool usage, and data pain points. This deep discovery process informed a comprehensive, prioritised data roadmap, outlining clear milestones, timelines, and business objectives.
By adopting an iterative approach, improvements could be implemented in logical, manageable phases,ensuring momentum without disruption.
Evolution Not Revolution: By the end of the engagement, La Fosse had a clearly defined data roadmap, a set of recommendations for optimising their data stack, and a well-structured plan for moving forward. Rather than making disruptive changes to their existing systems, they now had a pathway to incrementally improve their data capabilities while ensuring alignment with business objectives. This structured approach enabled them to enhance data accessibility, improve reporting efficiency, and empower teams across the organisation to make better, data-driven decisions.
Creating Value For La Fosse...
We evaluated 34 data initiatives,
Across 7 different departments,
To create one data roadmap.
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