Financial Services - Weely

Weely, as a fast-growing FinTech company, faced typical challenges in data analytics prevalent among scale-ups. Since the DevOps team was overwhelmed with work, Weelyʼs product and business teams handled manual data processing and ad-hoc reporting requests by themselves. This led to a landscape of unscalable, partially open-source tools and an overall architecture that required more money to maintain than the value it generated through insights. Plus, when insights led to new questions there wouldnʼt be enough data nor skills to dive deeper and conclude on the matter. Many questions remained unanswered, negatively impacting their growth and competitive edge.

After our initial discovery, shown in the image below, Weelyʼs leadership team was not surprised by what we found. For CTOs in the FinTech space, this challenge is all too familiar—balancing rapid growth with the need for robust, scalable analytics solutions. However, the need for a change became more than clear, by looking at the many problems we had identified.

One of the greatest problems identified in the initial discovery was the manual and unscalable process to curate and visualise data using AWS Athena and Metabase. Just maintaining an impact analysis of product feature releases on the revenue performance of customer cohorts for example, took half an FTE of the product team to maintain. Many manual steps were required to extract data from source systems, storing it in Amazon S3, modelling and visualising it with AWS Athena and Metabase. Any new requests from business users would either take weeks to realise or were completely ignored.

Weelyʼs unscalable data infrastructure required a central data platform to streamline their data processes and free up precious time from non-technical teams to reinvest into analysing and improving the business. Based on our initial discovery, we proposed the Snowflake Data Cloud as a solution, addressing issues like data silos, poor performance, and scalability. On top of that, we introduced Veezoo as a suitable solution to eliminate the need for unscalable dashboard development by making data explorable for nontechnical business users through an ‘ask dataʼ approach.

Through our structured evaluation process we fully automated the above described use case from data extraction through modelling and curation in Snowflake to scalable business analysis using Veezoo.

Thanks to this 4-weeks project, Weely was able to make a swift, informed decision, setting the stage for a robust data infrastructure that could support their ambitious growth plans.

Lacking deep data warehousing expertise, Weely needed a rapid and reliable setup of their data platform that suited their long-term analytics roadmap. Our Data Cloud Kickstarter provided a turnkey solution. Based on a brief technical Snowflake platform deep dive and our industry-specific architecture templates, we defined Weelyʼs future Snowflake account architecture in a 1-day workshop. Our Data Cloud Kickstarter tool would afterwards automate the account setup, saving Weely multiple days of intensive research and setup work.

This approach ensured a scalable, flexible foundation, accelerating the implementation and enabling Weely to start deriving actionable insights almost immediately.

Building internal BI solutions posed challenges for Weely, such as ensuring data quality, building a common understanding, automating data pipelines and efficient access to data insights. Our Internal Intelligence Suite Builder addressed these pain points by delivering tailored services that enhanced Weelyʼs data integration, cleansing, curation and sharing processes. This resulted in faster, more reliable analytics outputs, empowering Weelyʼs team to make better, data-driven decisions and identify new growth opportunities that would have otherwise never been discovered.

This was made possible through the step-by-step implementation of a modern data stack including Airbyte for extracting and ingesting data from source systems into S3, Snowflake and dbt for data integration, transformations and pipeline orchestration as well as Veezoo for scalable access to data for business users. With an out-of-the-box integration of dbt with Gitlab, any data transformation and quality assurance processes would be automatically version-controlled and documented. A common business understanding of the data was documented and shared through Confluence on top of that. Overall, this architecture formed a flexible foundation, keeping the data team lean and ensuring a high ROI on any future analytics endeavours.

With a solid internal analytics framework, we helped Weely to see the potential of sharing insights directly with customers and partners. However, scaling embedded analytics presented new challenges. Our  Embedded Evaluator guided Weely in defining the use case and selecting the right tech stack, ensuring a faster, better-informed investment decision. In a 4-weeks project, we implemented a first use case of a customer-facing dashboard embedded into Weelyʼs live application and tested with some of their customers. The feedback they got from this alongside with their confidence in their embedded analytics capabilities opened new avenues for customer engagement and value creation.

It was made possible with the combination of Embeddable and Cube on top of the already built central data platform with Snowflake. The use of Embeddable enabled non-technical product and customer success teams to build analytics experiences for customers using a no-code builder while the DevOps team governed the datasets and dashboard components that could be used. At the same time, the overall solution could be realised as a native embedding which meant a seamless experience for the end user. Ultimately, with Cube as the semantic and caching layer in between, high performance and efficient query management was guaranteed.

Developing, deploying and launching Weelyʼs full embedded analytics solution involved overcoming the final challenges on the outcome-driven analytics journey such as data privacy, security, and commercialisation complexities. Our Embedded Realiser package streamlined this process, helping Weely to bring their solution to market swiftly. After a successful launch, this new offering started generating new revenue for the business, making Weely ultimately achieve maximised ROI on their data investments. At the same time, it also showcased how previous steps laid the necessary groundwork for this high-value offering.

The ultimate solution was a set of comprehensive dashboards answering the customers most important business questions and letting them slice and dice the data as needed.

It is today a main differentiator for Weelyʼs product offering and their customers love its intuitiveness and insightfulness. Explore our embedded analytics demo to see these solutions in action.

By following our comprehensive Analytics Journey-as-a-Service, Weely transformed their data capabilities, driving significant business value and competitive advantage.