A revenue-ready data product in 8 weeks
Objective
Build a data product that unifies readership and email engagement data .
Obstacle
Data spanned multiple systems across multiple companies in various formats.
Outcome
Reliable models, clear metrics, and interactive dashboards that now drive smarter decision-making.
Background
BlueMatrix provides research distribution and analytics to investment firms and banks. They were sitting on a wealth of data they wanted to commercialise, but a lack of cohesion and no single source of truth was slowing down decision-making for product managers and executives.
This not only limited the speed at which BlueMatrix could respond to client needs, but also risked undermining confidence in their data-driven products. A modern data platform was urgently needed to unify definitions, streamline access, and enable automation of previously manual tasks. They turned to 173tech with a target to product an MVP product in just eight weeks.
Challenges
Two Companies: BlueMatrix’s data landscape was fragmented across two organisations, its own internal systems and those of its recent acquisition, Street Context, with each running on different infrastructure and technology stacks. This meant that operational teams often had to stitch together reports manually, relying on inconsistent extracts from multiple sources. These manual workarounds created bottlenecks in day-to-day reporting and, more critically, introduced ambiguity around the definition of core business metrics such as readership, engagement, and distribution performance.
Client Demand: Clients expected to see detailed insights into how documents were being consumed; who was reading them, what topics were gaining traction, and how engagement varied across distribution lists. Meeting this expectation required more than static reporting; it demanded interactive, scalable dashboards that could handle growing data volumes and evolving business requirements. Governance, version control, and CI/CD processes were critical, not optional. Without them, every change would risk breaking dashboards or producing inconsistent results.
Solution
MVP: From the outset, our priority was to deliver something solid, reliable, and genuinely useful that stakeholders could see and test quickly. To achieve this, we anchored the stack in Snowflake with dbt for data modelling and transformation, and used Metabase as a lightweight, fast-to-deploy BI tool for rapid prototyping. Once the MVP was validated, we transitioned the most critical dashboards and models into Looker, which offered richer functionality and scalability for production-grade analytics and client-facing features. By taking this “start simple, then tailor” approach, we ensured early value delivery while laying the foundation for a robust, long-term solution.
Multi-tenancy: Because this was ultimately a client-facing product, interoperability and scalability were just as important as speed. The solution needed to work seamlessly across BlueMatrix’s existing environments and integrate with their pipelines, including multiple clusters and Kafka topics spread across regions. We also had to account for multi-tenancy: ensuring that models, filters, and dashboards could support multiple firms, users, and topics without breaking consistency or exposing the wrong data.
The Proof Is In The Numbers...
8
£335k
10m+
Implementation
Fast Implementation: We implemented a scaleable data stack, centralised and modelled core data and created the first version of the product in just eight weeks. Most importantly, the MVP unlocked entirely new revenue streams. The unified data stack powered client-facing dashboards on document engagement, topic trends, and distribution-list performance, features that could be embedded directly in BlueMatrix’s platform as part of their commercial offering. These analytics not only strengthened retention by proving value to existing customers but also opened the door for premium subscriptions and new pricing tiers, tying the data product directly to top-line growth.
Outsourced: By leveraging 173tech’s data team instead of building in-house, BlueMatrix avoided the need to hire three full-time data specialists over 9–12 months, an investment estimated at £300,000. Instead, the MVP was built in eight weeks for just 16.3% of that cost, freeing capital to invest directly in growth initiatives rather than infrastructure.
Optimisation: We cloned databases for faster validation, introduced macros to minimise downtime during materialisation, and aligned Looker deployments and dbt branching within existing processes, all to enable BlueMatrix to scale this product in the future. Within 1 weeks of completion, the first external customer was scheduled for onboarding.
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