Rationalising 400+ Reports with Modern Cloud Analytics

Our client, a national organisation supporting people with intellectual disabilities, had transactional data spread across numerous source systems, the by-product of a number of group mergers and restructures over the past decade. With approximately 4,000 staff serving over 4,000 clients, and more than 400 operational reports in circulation, the data landscape was fragmented and inefficient.

Operational reports were critical to the running of the business and routinely contained sensitive data, including health information. The technical team were in a state of flux: cloud services had been provisioned but with little or no security controls in place, and no operational model for how these services could be used for business purposes.

Previous attempts to rationalise reporting and implement cloud data platforms had been unsuccessful, and the data team's maturity and strategic outlook needed to be aligned with leadership's vision.

DataSing's remit began with a current-state review, which was completed within two months and included recommended options for the future state. This progressed into the design and provisioning of a scalable platform built on Azure and Snowflake with a medallion data architecture.

We configured Snowflake with appropriate information security controls and began the incremental development of core data domains and topics that are repeatedly used by business analysts. We worked closely with the client's data team to show how to plan data initiatives with clear links to business opportunities and organisational priorities.

Our role at this client covers a lot of ground: from enabling executive-level discussions and managing direct relationships with technology vendors on the client's behalf, to data team mentoring, operating model advisory, and hands-on Snowflake and Azure technical delivery.

We introduced a quarterly planning concept aligned with agile programme increments, giving the data team both near-term and longer-term direction with clear lines of responsibility and accountability.

The client now spends considerably less on Snowflake credits than before our engagement. Critically, all members of the data team understand the workloads that drive costs, and managers have the tools to monitor spend effectively.

There are fewer operational reports being produced, and those that are created take less time. The first data domain was embedded into existing reporting within four weeks of platform delivery, demonstrating quick time to value.

DataSing introduced a planning framework that has given the data team structured direction for the first time. The project was delivered on time and within budget, leading to a long-term agreement for ongoing strategic advisory, data engineering, and platform management services.