The Cybex Retail Data Platform

From data chaos to unified foundation

Every retail organisation faces the same fundamental challenge: data is everywhere, but insight is nowhere. Sales data lives in the POS. Inventory lives in the WMS. Customer information lives in the CRM. Product details live in the PLM. And nothing talks to anything else.

Teams waste countless hours reconciling these systems, often producing reports that are out-of-date the moment they're finished. Every new AI initiative starts by rebuilding the same data pipelines. Every analytics question triggers another round of "which number is right?"

The Cybex Retail Data Platform solves this problem at its root. It's a retail data warehouse built for AI: one governed, documented model, purpose-built for retail. It ingests data from every source, conforms it to a unified model, validates it for quality, and has it ready for every application and user in your organisation before the business day starts.

Core capabilities of the Data Platform

The Data Platform is the foundation on which every Cybex application is built. Its capabilities determine what's possible across the entire AI ecosystem.

Most AI initiatives fail not because of bad algorithms, but because of bad data. Fix the foundation first, and every downstream project gets faster, cheaper, and more accurate. The thesis of this platform

Universal data integration

Connect to any retail data source, regardless of format, location, or technology stack.

• Pre-built connectors. Out-of-the-box adapters for major POS systems, e-commerce platforms, ERP systems, payment processors, and more, no custom coding required.
• Nightly governed loads. Every source loads overnight through documented ETL and views, finished before stores open, so every team starts the day on the same numbers.
• API & file-based integration. Flexible ingestion methods including REST APIs, webhooks, SFTP, cloud storage, and direct database connections.
• Custom adapters. For legacy or proprietary systems, we build custom connectors that map native schemas to the Cybex unified data model.

Unified data model

Transform disparate data structures into a single, consistent view of your business.

• Retail-specific ontology. The data model is purpose-built for retail, with native concepts like SKU, location, channel, customer, transaction, and inventory movement, not generic tables and columns.
• Dimensional consistency. Ensure that "product category" means the same thing in merchandising, finance, and operations. Eliminate the confusion of different departments using different hierarchies.
• Historical tracking. Maintain complete audit trails and change history for every data element, enabling time-travel queries and regulatory compliance.
• Extensibility. Add custom attributes, hierarchies, and relationships to support your unique business model without breaking the core platform.

The data models

Ten governed datasets sit on the unified model, one per retail function, and every BI analytics series and AI insight reads them. Each page sets out the grain, the attributes BI and AI read, the functional areas served, and the workflow from source record to decision.

Unified data flow

AI-powered data quality

Ensure that every downstream application and model is built on trustworthy data.

• Automated validation. Machine learning models continuously monitor incoming data for anomalies, outliers, and inconsistencies, flagging issues before they pollute the platform.
• Duplicate detection & resolution. Identify and merge duplicate records (customers, products) using probabilistic matching algorithms.
• Missing data imputation. When data is incomplete, AI models intelligently fill gaps based on historical patterns and business rules rather than leaving fields blank.
• Cross-system reconciliation. Automatically match and reconcile records across systems (e.g., linking a web order to its POS return) to maintain referential integrity.

Performance & scale

Handle enterprise-scale data volumes with sub-second query performance.

• Columnar storage. Optimised data structures that support fast aggregation queries across billions of transactions.
• Intelligent caching. Frequently accessed data and query results are cached in-memory for instant response times.
• Parallel processing. Distribute complex queries and AI model training across multiple compute nodes for maximum performance.
• Auto-scaling. Cloud-based deployments automatically provision additional resources during peak periods (e.g., Black Friday) and scale down during quiet times.

Security & governance

Protect sensitive data while enabling appropriate access across the organisation.

• Role-based access control. Define granular permissions at the user, role, and data-attribute level: ensure store managers see only their store, executives see everything.
• Data masking & anonymisation. Automatically redact or hash sensitive fields (PII, payment details) based on user permissions and regulatory requirements.
• Audit logging. Track every data access and modification for compliance and forensic analysis.
• Encryption. Data encrypted at rest and in transit using enterprise-grade standards.

Why the Data Platform matters for AI success

Most AI initiatives fail not because of bad algorithms, but because of bad data. The Retail Data Platform ensures that every model, dashboard, and application has the foundation it needs to succeed.

Deployment approach

We partner with your IT and data teams to deploy the Retail Data Platform tailored to your specific infrastructure and data landscape. A typical project follows three phases.

The Data Platform is not a destination; it's the foundation every subsequent decision is built on. Get the foundation right, and the rest of the platform compounds. Get it wrong, and every AI initiative pays the tax.