Background

As a data-driven organisation, we have a tremendous opportunity to leverage our data assets to drive business growth and gain a competitive edge. By investing in a strategic data engineering project, we can optimise our data infrastructure, reduce costs, and unlock new revenue streams. This business case outlines a proposal to modernise our data pipeline, enabling faster data processing, improved data quality, and enhanced analytics capabilities.

Problem Statement

Our current data pipeline is struggling to keep pace with our data's growing volume and complexity. The existing infrastructure is fragmented, leading to data silos and inconsistencies. This has resulted in:

• Delayed reporting and missed opportunities for timely decision-making
• Increased operational costs due to inefficient data processing
• Difficulty in scaling to meet future data demands

These challenges hinder our ability to leverage our data assets and make informed business decisions. Maintaining the status quo is no longer viable if we want to remain competitive in today's data-driven landscape.

Project Description

The proposed data engineering project aims to modernise our data pipeline by implementing a scalable and efficient data processing framework. The solution will involve:

• Migrating data from disparate sources into a centralised data lake
• Implementing a distributed data processing framework (e.g., Apache Spark) to parallelise data pipelines
• Establishing data quality checks and validation processes to ensure data integrity
• Developing a self-service analytics platform for business users

By leveraging cutting-edge technologies and best practices, we can streamline our data workflows, reduce processing time, and enable real-time analytics. This will empower our organisation to make data-driven decisions faster and more confidently.

Strategic Alignment

This project aligns with our strategic goal of becoming a data-driven organisation. By modernising our data infrastructure, we can:

• Gain deeper insights into customer behaviour and preferences.
• Identify new revenue opportunities and optimise pricing strategies.
• Improve operational efficiency and reduce costs.