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Data Engineering

Objective:

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.

Challages

Challenges

  • Patient Data : Duplication, Mismatched Entries, Valid Details
  • Client: Having different discounts, MRP for a particular locality
  • Tests: Unordered tests, Demand, Utilization of machines
  • Sales: Difficult in predicting Sales for particular test
  • Cost: No visualization for business Decision(Expenditure on a lab)
  • Bulk Data Processing: Volume of data, a significant bottleneck

Solution

Possible Solutions

  • Cleansing Patient Data: Implemented processes to cleanse raw data, ensuring accurate contact details, unique identifiers, and historical visit records.
  • Client Pricing Strategy: Tailored discounts and pricing based on demand and lab performance.
  • Optimising Test Portfolio: Removed redundant tests, promoted package tests, and optimised pricing based on location and demand.
  • Sales Performance Analysis: Provided insights into test demand and lab performance to facilitate sales forecasting.
  • Cost Visualisation: Developed visualisation tools to analyse lab expenditures per test and machine, aiding in business decisions.
  • Bulk Data Processing: Pre-processing data to alleviate bottlenecks and ensure faster computation.

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.

Visualisation

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:

  • Reduced final data volume by 30%, enhancing data quality.
  • Improved accuracy in pricing and discounting, resulting in enhanced client satisfaction.
  • Error-free data uploads, ensuring consistent outcomes.
  • Generated actionable insights for the business team, identifying gaps and areas for improvement.
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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