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CASE STUDY February 17, 2022

Enabling Easier Data Consumption and Improving Data Lake Adoption for a Fortune 500 Logistics Service Provider

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Business Objective

Our client is a leading US-based logistics service provider offering logistics management, trucking, warehousing, freight forwarding, brokerage, supply chain management, distribution services, etc.

The client had multiple data sources feeding into their data lake but the data dictionary and understanding of data lineage was missing. They didn’t have a silver layer having clean and standardized data before moving into the data lake. Besides, the performance of Databricks and Power BI was not good. All of these led to an incomplete road map for data lake adoption. The client wanted our help in cleaning the data from the raw/bronze layer and move it to the silver layer from where the data can be pulled by the data science and analytics team.

Challenges

  • Gathering information from multiple teams and stakeholders
  • Building governance processes, data vetting, and legal compliance of data
  • Handling data inconsistencies across multiple sources
  • Building a flexible, configurable framework that allows easy addition of data sources in future
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