Transformative data engineering for a major retail chain

Background:

Client: A very large retail chain which is a household name with 100 of stores across regions and operations in Food and Beverage, Apparel, fuel and forecourt,  finance and insurance etc 

Challenge: The client aims to do a large data migration and to leverage their data effectively to optimize operations, enhance customer experience and minimise TCO. They face key challenges in the data quality and standardisation , integrating and analysing vast amounts of data  generated by their stores, online platforms, supply chain, and customer interactions.

Objectives:

1. Data management, quality and governance : Establish best in class data management policies , processes and tools, align to  the data principles and strategy defined by CDO, Implement processes and tools to ensure data standardisation and quality.
2. Data integration: Integrate data from various sources including legacy mainframe sources  ERP systems , point-of-sale (POS) systems, customer relationship management (CRM) tools and external data sources.
3. Data lake : Develop a centralized data store for structured and unstructured data in a scalable and accessible manner, so that it can be leveraged produce actionable insights.

Proposed solution:

1. Data management framework: Establish data management and  governance policies, data architecture standards and processes , data  definition and modelling tools ,metadata management, and data lineage tracking , define and implement data security standards policies and processes leveraging Azure , Erwin, data vault 2.0  etc
2. Data quality framework: Implement a custom framework to ensure data quality, standardization and consistency. The solution was a pySaprk based custom-built solution leveraging Azure data bricks platform and a rule engine 
3. Data integration platform: Implement a metadata driven extract load transform platform leveraging Azure data factory , Databricks platform , metadata driven pySpark to extract data from various sources, load into Azure data lake storage  transform it into a unified format, and load it into data lake/modelled tables .
4. Data lake : Deploy a cloud-based data lake  solution leveraging azure data bricks Lakehouse architecture , Azure Synapse  etc 

Implementation plan:

1. Since this was part of a large transformation program which includes business transformation  data and application migration , application rationalisation  etc the program followed the strategy outlined by CIO/CTO
2. Assessment and requirements gathering: Conduct a thorough assessment of existing data infrastructure, sources, and business requirements. Define clear objectives and success criteria for the data engineering project.
3. Design: Define data management and data quality frameworks and processes in line with strategy, principles, business requirements and business architecture. Design phase also involves 
   • Design a data architecture framework to support meet business requirements and support effective data strategies and data-driven decisions 
   • Design a scalable, resilient and performant reusable data integration platform architecture to meet the requirement 
   • Design a reusable , scalable and efficient data integration engine  to support the volume , Variety and velocity of data 
   • Design an effective data quality framework to ensure veracity of the data 
   • Implement a data security architecture meet modern day security threats 
4. Development and testing: Develop data models ,  ETL pipelines, data quality routines , data migration routines , implement data security rules  according to the design specifications. Establish quality gates and perform thorough testing (both automated and manual ) to ensure quality of deliverables  .
5. Environment management and deployment : Establish different environment to deploy the data engineering solutions integrate them with existing systems and processes. Monitor performance, troubleshoot issues, and optimize as necessary.
6. Training and knowledge transfer: Provide training sessions for client stakeholders on using the data engineering tools and platforms effectively. Document processes, best practices, and guidelines for ongoing maintenance and support.
7. Continuous Improvement: Establish mechanisms for continuous monitoring, feedback, and improvement of the data engineering infrastructure and processes. Stay updated with emerging technologies and industry trends to enhance capabilities and deliver value to the client.

Outcome and benefits:

1. Improved quality and value of data assets : Client can trust the data to be used for decision making and regulatory reporting .
2. Reduced total cost of ownership : Enhanced value provided by the data along with efficient data management process and tools minimise total cost of data ownership.
3. Improved availability and democratisation of data 
4. Enhanced data security and regulatory compliance : Data governance policies ensure compliance with data protection regulations and enhance data security and privacy.
5. Scalability and Flexibility: Cloud-based data infrastructure allows for scalability and flexibility to adapt to changing business needs and accommodate future growth.

Conclusion:

By implementing a flexible management framework and data engineering solution , the retail chain and harness the full potential of their data assets for their competitive advantage and drive innovation and cost optimization.