Challenge / Goal
A global multinational oil corporation involved in the refining, storage, supply and retail distribution of a wide range of petroleum products sought enterprise-wide visibility and data management to expedite growth. Executing enterprise programs was heavily reliant on data from multiple systems including on-prem ERP, cloud CRM, terminal management systems and fulfillment systems. This voluminous data created critical challenges which the company tapped Bridgenext (formerly Emtec Digital) to solve.
- No singular consolidated view of enterprise data owing to the absence of a centralized model and siloed operations across multiple geographical regions
- Lack of effective enterprise-wide data governance leading to an inability to define a streamlined decision-making process
Solution
The primary task for our data engineering team was to streamline the data aggregation process by integrating data from diverse geographical locations to obtain a single source of truth for informed decision-making. We implemented an enterprise-level data lake store to pull as-is data from different systems into a single layer and then push it to the data warehouse. Our robust solution provided actionable insights across the enterprise data landscape.
- Data pulled in from 140+ countries and 10 different regions worldwide
- Different data types and structures supported
- Data catalog and data lineage in development phase
- ~7 Terabytes of data over five years from 18+ enterprise systems consolidated in a single repository
- Current implementation protocol is a common analytics model for data aggregation
Results
Our agile enterprise data lake architecture enabled the client to acquire a 360-degree view of enterprise data across geographically scattered systems for better processing. By implementing a sound enterprise data management strategy, the client was able to run analytics on vast amounts of data from various structured and unstructured system sources and garner actionable decision-fueling insights.
There was a notable improvement in data operations through a uniform operational strategy defined and implemented across multiple regions. Our data lake solution further enabled the client to build and run centralized data governance models and create a standardized methodology for data acquisition and processing.
