Data Engineering


Transforming CRM and RNI Systems into Strategic Goldmines

Our objective was to streamline and optimise data engineering processes for Customer Relationship Management (CRM) and Request for New Investigation (RNI) systems. We aimed to ensure data purity, personalise client strategies, refine test portfolios, enhance sales forecasting, analyse costs effectively, and overcome bulk data processing hurdles, ultimately enabling data-driven decision-making and fostering business growth.

Problem Statement:

The existing data systems faced challenges such as complex data structures, our existing systems encounter persistent challenges, including data duplication, inconsistent entries, and a lack of predictive analytics capabilities, hindering efficient data utilisation for strategic decision-making.




Possible Solutions

Design Process

Data Extraction

Extracted raw data from SQL Server databases, including patient, test, client, and organisational data.

Data Transformation

Utilised Spark and Hive for transforming raw data, cleansing, filtering, and structuring datasets.

Data Loading

Loaded transformed data into HDFS or S3 in CSV format for accessibility and further analysis.


Utilised tools like Power BI and Tableau for reporting and visualisation.

Tools Used

Tools Used

SQL Server for data extraction.

Spark and Hive for data transformation.

HDFS and S3 for data loading.

Power BI, Tableau for data visualisation.

Business Benefits:

By optimising data engineering processes, we have empowered our systems to deliver accurate insights, facilitate informed decision-making, and enhance overall business performance.

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