How a global F&B operator with 3,000+ venues across 40 countries replaced fragmented tooling, eliminated knowledge silos, and gave every team — from data engineers to front-line staff — real-time access to the intelligence they needed.
SSP Group plc is a leading global operator of food & beverage venues in travel hubs — running over 3,000 units across nearly 40 countries and employing around 49,000 people. Specialising in airports, train stations, and other transit locations, SSP delivers a diverse range of outlets from sit-down restaurants and cafés to bars, quick-service restaurants, lounges, and food-led convenience stores.
The company's brand portfolio balances international, national, and local concepts to suit the needs of varied clientele. Operating at this scale means SSP manages enormous volumes of data every day — tracking business performance, designing marketing strategies, and launching campaigns across dozens of markets simultaneously.
"Colrows is a complete solution for a firm like SSP Group; it turned out to be a one-stop solution for us."
Jayesh Pawar — Head of Analytics, SSP GroupSSP Group manages large volumes of data related to local events, business performance, and community insights — all critical for daily decision-making. As the business grew, so did the complexity of managing that data, exposing deep inefficiencies in the tools and workflows teams relied on.
The tech team had no standardised approach to database management. Developers were independently using different clients — DBeaver, Squirrel, Toad — creating a fragmented environment where knowledge was siloed and work could not be reliably shared. Meanwhile, front-line staff couldn't access real-time data at all, forcing them to queue requests to already-stretched engineering teams.
SSP attempted to solve the front-line access problem by building an internal system. But as the business scaled, the system became a burden — requiring constant updates and refinement it could never sustainably keep up with.
Compounding everything: SSP was simultaneously building out a new data science team and needed Python Notebooks capability integrated into the same environment — not bolted on as a separate tool.
Developers used DBeaver, Squirrel, and Toad independently — no shared approach, no standardised query management.
Separate script copies per developer meant version mismatches, lost context, and no reliable way to track progress across teams.
Customer-facing staff had no self-serve access to data, forcing reliance on engineers to fulfil basic inquiries — slowing issue resolution.
A proprietary front-line data tool required constant maintenance and couldn't scale with growing business demands.
The nascent data science team needed Python Notebooks integrated into the same workflow — not as a separate, unconnected environment.
Secure database access required maintaining bastion infrastructure — adding operational overhead and slowing developer setup.
SSP's problem wasn't any single tool gap. It was the accumulation of many tool gaps, each addressed independently, each adding to the fragmentation. Fixing one layer — say, standardising the SQL client — wouldn't resolve the front-line access problem, or unify the data science workflow, or eliminate the bastion host. SSP needed a platform, not a patch.
Each new tool creates a new integration surface — authentication, data routing, access controls — that compounds over time.
Engineers move between environments, losing shared context. Notebooks don't know what the SQL editor ran. Front-line tools have no visibility into analyst work.
Every tool has its own user model. At SSP's scale, synchronising permissions across tools becomes a full-time operational task.
Queries, scripts, and analyses built in isolated tools walk out the door with the individual — not the organisation.
SQL notebooks, Python environments, and conversational data access live in one platform — all pointing at the same data, with consistent permissions.
Shared SQL notebooks mean work is visible, reviewable, and reusable across the team from day one — not after a migration project.
Colrows eliminates the need for bastion host infrastructure, reducing operational overhead and simplifying developer onboarding significantly.
Data science workflows sit inside the same environment as SQL and conversational analytics — no separate toolchain, no context switching.
Colrows addressed SSP's challenges not by replacing one tool at a time, but by providing a single unified environment across the entire data workflow — from database engineering to executive insight to customer-facing issue resolution.
Replaces DBeaver, Squirrel, and Toad with a single, collaborative SQL environment. Queries are shared, versioned, and searchable — not siloed in individual machines.
Front-line staff query data in plain language without writing SQL or waiting for engineering support — enabling real-time answers at the point of customer interaction.
Natively integrated with the same data sources as SQL, allowing SSP's data science team to run complex analyses and build models without leaving the platform.
Data and query results shared directly into Slack channels — embedding analytics into existing communication workflows rather than requiring teams to switch context.
The change wasn't incremental. Deploying Colrows replaced an entire layer of fragmented infrastructure — and the effects rippled through every team that touched data at SSP.
Net effect: 40% reduction in data management overhead — engineers spending time on analysis, not infrastructure.
Net effect: front-line teams operating with real-time data independence — no engineering bottleneck, no queued requests.
"There are no tools currently available on the market that compare, and the pricing is incredibly reasonable."
Jayesh Pawar — Head of Analytics, SSP GroupThe deployment didn't just improve efficiency metrics — it reframed what SSP's data infrastructure is capable of. Every team, from engineers to customer success managers, now operates with the same data fluency that previously required deep technical expertise.
Standardised tooling and centralised notebooks eliminated the operational cost of managing fragmented, per-developer environments across the engineering team.
Conversational data access allows customer-facing teams to resolve inquiries in real time — without queuing requests to engineers or waiting for data pulls.
Shared SQL notebooks and Slack integration transformed how teams work together — moving from isolated work streams to a single, visible, collaborative environment.
SSP's custom-built front-line data tool was retired entirely. Colrows replaced it — eliminating ongoing engineering maintenance costs and technical debt at scale.
What SSP needed wasn't a better SQL client, or a smarter front-line tool, or a new notebook environment. They needed a platform that understood how these capabilities belong together — and built them as a coherent whole rather than a collection of integrations.
Colrows provides a unified data platform in which SQL editing, Python notebooks, conversational access, and collaboration tooling share the same authentication layer, the same data sources, and the same institutional memory. Analysts, engineers, data scientists, and front-line staff all operate in the same environment — with appropriate access, not separate tools.
The result is a system where knowledge compounds. Queries built by engineers surface in notebooks used by data scientists. Insights from analysts flow directly to front-line teams via the conversational interface. Work done today becomes institutional infrastructure — not a file on someone's laptop.