A global pharmaceuticals organization enabled Kubrick to modernize their data architecture in order to unlock new analytics capabilities which create supply chain efficiencies that save millions.
Data points connected via knowledge graph
1Bn
Legacy reporting dashboards result
100%
Time saving in reporting
25%
KubrickSupply Chain Analytics
The challenge
- The client's R&D team faced largescale challenges in their supply chain due to storage and transportation inefficiencies, resulting in excessive costs, wasted products, and inconsistencies in shipping methods which resulted in poor visibility to predict supply and increased environmental impact.
- They were inhibited from advancing their predictive analytics by costly legacy data architecture, which caused siloes in their data sources and was too slow to supply the near-real-time data required for insight to govern their supply chain.
The solution
- We helped scope an all-in-one solution to migrate their data to a Snowflake Data Lake, from which they could supply near-real-time data to create a knowledge graph with Neo4j.
- Kubrick utilized their Accelerator program to supply 30+ Data Engineers to provide a POC in a rapid 2-week sprint, which unlocked budget for the R&D team, as well as secure additional funding from our partners at Snowflake.
- Kubrick then deployed 2 self-managed squads of consultants, guided by our inhouse Snowflake and Neo4j experts, to undertake the mass migration and analytics product builds in just 1 year.
The results
- Modern data architecture capability with Snowflake: We redesigned and rebuilt all data into Snowflake's Data Lake capability, creating real-time data ETL from all data sources into a single platform for improved data accuracy and access.
- Advanced analytics with Neo4j: We used graph technology to reveal the interconnectivity and inefficiencies across the supply chain, connecting 1 billion data points from which to gain insight on mass scale.
- The project enabled the client to save millions in shipping time, reporting time, and reducing waste, while derisking their supply chain and data governance.
