Case Study

Achieving 95% less code with semantic SQL queries

Published on 05th January 2022 | By timbr

For

International CPG enterprise

Tags

Virtual Knowledge Graph
Graph Data Management
Data Modeling

Challenges

Unorganized data was at the core of the challenges that the corporate faced. There were three major issues:

  • Sales data was gathered from various sources and belonged to multiple datasets. Hence, it was scattered across multiple datasets.
  • Different naming conventions were followed across all countries that the client operated in.
  • Data gathered from analytical queries was considerably complex.

  • Solutions

    timbr solved the multi-layered problem by harmonizing the scattered data.

  • It imported the conceptual model (data catalog) from the ERWIN modeler system into a virtual Knowledge Graph connected to the underlying databases.
  • It aligned business terminology by unifying data from all operating countries with different naming conventions and made it accessible worldwide. The data was classified as Brand & Product based on Categories, Packaging, Channels, and Pricing.
  • It facilitated data modeling and classification for consumers to easily query different levels of granularity, while also facilitating data complexity in analytical queries for the client to gain actionable insights.

    The platforms' distinctive capabilities like Graph Analytics and Ontology Modeling allowed declaring an enterprise-level, overarching ontology that efficiently integrated data models from multiple sources and quickly performed complex graph analytics. SQL queries were easier to write, maintain, and debug, shortening more than 75% of the query length and significantly reduced the technical complexity and delivery time.

  • Key Metrics

    20X

    Achieved 20X faster time to value

    95%

    Achieved 95% less code with semantic SQL queries

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