Enabling data democratisation without any migration.
Objective
La Fosse aimed to make data accessible across teams without technical roadblocks.
Obstacle
Fragmented systems and niche recruitment tools made integration difficult.
Outcome
A structured roadmap streamlined their data strategy and future-proofed analytics.
Background
La Fosse is a recruitment firm specialising in data. They deliver a broad and tailored portfolio of IT and tech recruitment solutions through a service built on care. Their specialist teams operate across the UK and internationally, placing top talent on both a permanent and contract basis. The company had an existing data setup and a small data team, but they were struggling with data democratisation beyond this. Their technology stack was relatively complex, and data was not yet fully integrated into every system, making it difficult to ensure accessibility across departments. They turned to 173tech to help provide a clear roadmap of the next five years.
Challenges
Democratisation: While La Fosse already had a data setup in place and a small internal data team, they faced challenges in making data accessible and usable across the organisation. Their data ecosystem was complex, with multiple tools and platforms in use, but data was not yet fully integrated into every system. This lack of integration meant that data-driven decision-making was limited, as teams struggled to access and analyse the information they needed in a seamless way.
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.
The Proof Is In The Numbers...
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Implementation
Iterative Implementation: To ensure a structured approach to improving La Fosse’s data capabilities, we conducted in-depth discussions with internal teams to assess their specific needs. This involved evaluating existing tools, identifying gaps in processes, and understanding data requirements; whether related to dashboards, automated reporting pipelines, or broader data governance initiatives. From these insights, we developed a comprehensive data roadmap, outlining clear steps, estimated timelines, and business priorities. The roadmap served as a strategic guide, ensuring that improvements were implemented in a logical and phased manner, with the most critical needs addressed first.
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.
