Data Integration into Corporate Data Lakehouse

Client

A global leader in consumer health

Challenge

The client struggled with getting a unified view of their business to support decision-making. The key challenges included:

Multiple, disintegrated data sources, including some legacy, proprietary systems,
Data of various types and modalities, including unstructured data,
Poorly managed master data.

Additionally, some of the unstructured data resided in legacy systems, which prevented the client from leveraging the latest AI-based solutions to unlock the knowledge hidden in these siloed systems.

They needed scalable and efficient data pipelines that would catalyse proper master data management and governance, and allow reliable data integration and analytics at a reasonable cost (considering large data volumes).

Solution: Data Integration into the Corporate Data Lake

1. Unified Data Access
2. Simplified Architecture
3. Scalable Ingestion Pipelines
4. AI-Powered Data Cataloging
5. Natural Language Knowledge Retrieval

A seamless data integration of the disparate sources into a centralized data lake (on top of the Databricks technology stack) to ensure a single source of truth and unify insights across the organization.
Leveraged modern data lakehouse architecture and applied a common integration pattern to simplify and standardize the data architecture.
Developed scalable pipelines to pull the data from various sources using multiple interfaces (including db connectors, APIs, file-based integration, etc.) with optimal cost-to-performance ratio.
Performed data cataloging and generated master data entities over AI-driven data pipelines to enable stronger data governance and management.
Enabled an advanced RAG on unstructured content to allow users to “talk to documents” and extract knowledge from large text sources.

Key Outcomes

Scalable data platform, following best practice for reference Lakehouse architecture, simplified the overall data architecture, making it easier to manage and build further on top of it.
Single source of truth and unified view of the business, enabling a significantly broader range of reporting and analytics use cases for decision making
Optimal cost-to-performance made even the most complex data processing affordable, unlocking a broader range of use cases.
GenAI technologies enabled rapid knowledge extraction from vast text databases, improving productivity and enabling new use cases for the R&D department.