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Many organisations struggle to fully understand and trust their data, particularly as they adapt to the digital age in pursuit of competitive advantage. Progress is often limited by weak data foundations, driven by insufficient focus on data governance and quality at the point of data creation. Data, like money or equipment, requires active management across its entire lifecycle. Without this control, organisations are unable to maximise value and are exposed to financial, operational, regulatory and reputational risk.
This project focuses on the importance of enterprise level data quality and data governance through the design of a data quality framework and executive summary, presented as a case study for EverTrend Retail Group (ETR) a large multi channel retail organisation. The case study demonstrates how data quality can be embedded at source across the full data lifecycle, supported by governance roles, metadata management, master data management (MDM), privacy controls and cloud enablement using AWS and Snowflake.
EverTrend Retail Group (ETR) operates across physical stores, e-commerce, and mobile platforms. Its rapid growth through mergers and digital expansion has resulted in fragmented and inconsistent data across core systems including Salesforce, Oracle ERP, Shopify and Excel. These challenges have led to poor data quality, analytics bottlenecks, increased regulatory risk and a limited ability to scale advanced capabilities such as AI-driven personalisation and dynamic pricing.
The framework focuses on critical data domains:
- Customer
- Product
- Sales
It prioritises data quality across six dimensions:
- Accuracy
- Timeliness
- Completeness
- Validity
- Consistency
- Uniqueness
- Enterprise data governance operating model (Data Owners, Stewards, Central Data Team, Data Privacy Officer)
- Data quality framework embedded across the data lifecycle (Data quality dimensions and thresholds)
- Metadata management (business glossary, data dictionary, lineage)
- Master Data Management (golden records)
- Privacy, consent, retention, and compliance controls (CCPA)
- Cloud-based architecture (AWS & Snowflake)
- Data maturity model (current vs target state)
- Data Quality Framework (Powerpoint)
- Executive Summary (Word)
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Establishing a shared data foundation starts with clear ownership, accountability and collaboration across the organisation.
Embedding data quality controls at the point of data creation and enforcing them across the end to end data lifecycle ensures data remains accurate, consistent and trustworthy. This approach establishes a strong culture of quality, reducing costly remediation efforts and preventing downstream errors before they occur.