Case studies

IDS designed and built a new analytical data mart purpose-built for Controlling, constructed entirely on big data technologies rather than the client’s Oracle stack.

When the migration to the new DWH began, IDS took on the task of carrying that Datamart — and the business logic behind it — onto the new platform.

IDS Consulting was brought in to provide hands-on technical support and consultancy for the migration to Databricks.

IDS Consulting was brought in not for a single fix, but as an ongoing extension of the client’s Data Warehouse team. The mandate: modernize how data moved through the platform, and keep it evolving safely as the business — and the underlying infrastructure — kept changing around it.

IDS delivered the platform end-to-end (Python and .NET services, automated test coverage from day one) while running a structured knowledge transfer track in parallel — pairing IDS engineers with the client’s internal team throughout, not just in a handover week at the end.

IDS delivered full-stack development (Java backend, React front end) with a dedicated QA workstream running in parallel from the start — functional, regression, and performance testing built into every sprint — under a single programme manager accountable for scope, timeline, and quality together.

IDS ran a cost and architecture review across both AWS and Azure environments, then designed a rationalized multi-cloud strategy — keeping each provider for the workloads it did best, eliminating duplicated pipelines, and applying rightsizing and reserved-capacity planning where waste was highest.

IDS architected a cloud-native network analytics platform on BigQuery and Dataflow, built for streaming ingestion at telco scale, replacing nightly batch loads with near-real-time processing of network events.

IDS built a governed semantic layer on Delta Lake that normalized customer data models into a consistent structure the GenAI assistant could query reliably, then supported the internal team in standing up its own data science capability to maintain and extend it.

IDS rebuilt the fraud pipeline on governed transaction data, then operationalised the model using Databricks and MLflow for experiment tracking, versioning, and automated retraining, with Azure ML handling deployment and monitoring for drift.

IDS migrated the QlikView estate to Qlik Sense, rebuilding the semantic layer with a governed self-service model — buyers could explore sell-through, stock cover, and markdown impact themselves, without waiting on the BI team for every new cut.

IDS’s BI team was engaged after a referral from a data platform vendor, specifically for its documented migration path from Cognos to Power BI.