ETL Process Enhancement

Technologies used

Oracle

Oracle SQL

Motivation

The customer, a US national leader in healthcare insurance, claims, and patient safety solutions, faced issues with inconsistent and incomplete reference data from multiple operational systems, which caused reporting errors, redundant records, and delays in critical decision-making. Their goal was to ensure that the ETL process delivers the most complete, accurate, and consistent data, prevents overwriting by less reliable sources, eliminates duplicates, and maintains high performance, enabling reliable analytics, reporting, and operational workflows while improving scalability and efficiency.

Main challenges

Challenge 01

Data Inconsistency

The ETL process received data from multiple operational systems, some of which provided incomplete or less detailed information. This led to inconsistencies in the data warehouse, causing reporting errors, unreliable analytics, and potential misinformed decisions.

#1

Challenge 02

Duplicate and Redundant Data

More detailed attribute values from reliable sources were sometimes overwritten by empty or less complete values from other systems, creating duplicate or redundant records. This reduced the overall quality, integrity, and trustworthiness of the data.

#2

Challenge 03

Performance Limitations

The original ETL process had a long runtime, exceeding 30 minutes, which delayed reporting and analytics. Optimizing the process for faster execution without compromising data quality was essential for timely operational insights.

#3

Challenge 04

Scalability and Flexibility Challenges

The system needed to easily adapt to new data sources and changing priority rules for attributes. Without a scalable and flexible solution, any modification in data handling could disrupt ETL workflows or require extensive manual intervention.

#4

Our approach

Analysis and Prioritization of Attributes

We began by analyzing all the attributes from every source and assigning priority to each attribute based on the source's reliability and completeness.

Survivorship Matrix

We developed a Survivorship Matrix to clearly define whether to keep or overwrite an attribute value based on the source’s priority. This logic was incorporated directly into the ETL process, reducing the need for pre-processing.

Data Deduplication and Consistency

By implementing the Survivorship Matrix, we ensured data completeness, consistency, and reliability, while eliminating unnecessary overwriting of values. The approach also increased flexibility, allowing priority values to be easily modified if needed.

Performance Optimization

We ensured the total running time of the ETL process met performance requirements by reducing the time from over 30 minutes to less than 5 minutes, achieving a significant performance boost.

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Solution

01

Survivorship Matrix

A rules-based framework that determines whether to keep or overwrite attribute values from multiple sources based on priority, ensuring consistent and accurate data in the warehouse.

Key capabilities:

• Rule-based decision making for attribute retention
• Integration directly into ETL SQL logic
• Supports multi-source data aggregation
• Reduces errors from incomplete or inconsistent data

02

Data Deduplication

Removes redundant or conflicting data entries, ensuring that only the most complete and reliable information is loaded into the data warehouse.

Key capabilities:

• Eliminates duplicate and redundant records
• Preserves high-priority, accurate data
• Maintains historical data integrity
• Reduces manual data cleansing efforts

03

Attribute Prioritization

Assigns priority values to data attributes based on source reliability, improving ETL decision-making and enabling flexible adjustments without process disruption.

Key capabilities:

• Customizable priority rules for each attribute
• Dynamic handling of new data sources
• Supports future scalability
• Prevents loss of critical data

04

Performance Optimization

Improves ETL runtime by optimizing SQL logic and process flow, allowing large healthcare datasets to be processed efficiently and consistently.

Key capabilities:

• Reduced ETL runtime from 30+ minutes to under 5 minutes
• Supports high-volume healthcare datasets
• Efficient processing without data loss
• Enables timely reporting and analytics

Results & business impact

Improved Data Quality

Ensured the most complete, accurate, and reliable data is loaded into the data warehouse for analytics and reporting.

Enhanced Flexibility

Attribute priority rules can be adjusted easily, supporting scalable and adaptable ETL processes.

Optimized Performance

Reduced ETL runtime by over 80%, significantly improving operational efficiency and responsiveness.

Reduced Redundancy

Eliminated duplicate and conflicting records, improving data integrity for downstream applications.