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Summary – Modernizing the digital core has become essential in the face of rising competition and the unattainable promises of AI and omnichannel on outdated, monolithic, siloed IT foundations. By unifying customer and inventory data, mapping and governing data flows, migrating to a secure hybrid cloud, and breaking platforms into microservices, you gain real-time agility, reliability and scalability. Solution : core audit → quick wins on data unification and cloud migration → modular refactoring roadmap to support AI and omnichannel.
In an increasingly competitive retail landscape, heavy investment in artificial intelligence and omnichannel tools only pays off if the digital core—that is, the ERP, CRM, PIM, customer and inventory databases—can keep pace. Legacy architectures, data silos, and technical debt stifle competitiveness and render innovation promises illusory.
To make AI and omnichannel projects truly viable, it is imperative to first renovate the IT foundation: unify data, migrate to the cloud, and simplify monolithic platforms. This article explains why this modernization is a strategic priority for retailers and how to carry it out successfully.
Unify data for a 360° view
Without a unified data foundation, marketing and operational activities remain fragmented and ineffective. Consolidating customer and inventory streams is the cornerstone of the 360° view necessary to drive data-driven commerce.
Consolidation of customer data
Customer information is often scattered across CRM systems, brick-and-mortar stores, e-commerce platforms, and loyalty programs, each generating separate reports. This dispersion prevents a clear understanding of the purchase journey and the ability to anticipate needs in a personalized way. A unified pipeline can ingest, normalize, and correlate these data streams in real time, ensuring every channel relies on the same single source of truth. Open-source data orchestration solutions facilitate the setup of synchronous or asynchronous flows according to business constraints. Ultimately, this consolidation provides a solid foundation for predictive analytics and targeted campaigns.
Implementing standardized connectors combined with enrichment and cleansing scripts ensures data reliability. Validation routines can detect duplicates, invalid email addresses, or inconsistencies in order histories. Continuous monitoring of these pipelines issues alerts on flow interruptions or quality degradation, preventing business errors from propagating through the digital ecosystem. Adopting common formats (JSON, Parquet) also enhances interoperability with analytics modules or data science tools. By structuring data at ingestion, teams gain both agility and confidence.
By optimizing these streams, marketing and operations teams can tailor re-engagement, promotion, or replenishment scenarios based on a single source of truth. Costs associated with multiple data exports and manual corrections are reduced, while the time-to-market for new offers is shortened. Management via consolidated dashboards then becomes a true differentiator, helping to better steer advertising spend and inventory levels.
Map data flows
Mapping all exchanges between ERP, CRM, PIM, and other systems is often underestimated but essential. Without this map, IT leaders cannot pinpoint the exact origin of data or quickly identify potential breakpoints. Documenting data schemas and dependencies is the first step toward designing robust, scalable pipelines. Open-source data lineage tools automatically animate these maps, making them accessible to all stakeholders.
Once the topology is documented, you can rationalize flows by eliminating duplication points and redundant transformations. At that stage, formats can be standardized, file system changes minimized, and network performance optimized. This approach cuts infrastructure costs and simplifies maintenance. Moreover, by clearly identifying functional data owners, you establish shared responsibility between the IT department and business units.
When flows are controlled, upgrading software or moving to a new version of a critical component no longer disrupts daily operations. End-to-end automated tests replicate the entire cycle, ensuring reliability with every update. If needed, restores or rollbacks can be completed in hours instead of days, reducing the risk of service interruptions.
Governance and data quality
Data governance goes far beyond setting up a catalog. It involves defining clear ownership, lifecycle, and priority rules for each data type. Cross-functional data councils—comprising IT, marketing, and operations—approve new enrichment or removal policies. This transverse governance ensures aligned priorities and faster responsiveness to regulatory changes (GDPR, e-privacy).
A Swiss fashion retailer with around fifty stores consolidated its customer and inventory databases into a centralized data lake orchestrated by open-source pipelines. This example shows how rigorous governance can cut pricing errors by 30% and optimize real-time product availability. Marketing teams then launched hyper-personalized campaigns based on reliable data, achieving a conversion rate over 25% higher.
Establishing quality metrics and SLAs for data streams allows you to measure compliance with business objectives. Dedicated dashboards tracking quality incidents (duplication, missing data, time lag) alert managers in real time. As a result, data becomes a controlled asset capable of supporting advanced use cases without breaching operational or regulatory requirements.
Migrate to a secure, scalable hybrid cloud
The cloud offers an ideal playground to deploy extensible, secure, and resilient capabilities. A phased migration to a hybrid model ensures flexibility and cost control while strengthening compliance and security.
Choosing a hybrid cloud architecture
Opting for a hybrid architecture lets you keep critical workloads on-premises while leveraging on-demand public cloud resources. This dual approach enables shifting certain functions (analytics, machine learning, backups) to ultra-scalable environments without disrupting day-to-day operations. Containers and Kubernetes orchestration facilitate microservice portability between on-premises and cloud, ensuring a seamless operational continuity.
Setting up a secure virtual private network (VPN) or dedicated direct connections (Azure ExpressRoute, AWS Direct Connect) guarantees performance and confidentiality for sensitive data exchanges. You can then operate multiple clouds in parallel, spreading risks associated with outages or pricing fluctuations. This multi-cloud strategy avoids vendor lock-in and aligns your IT approach with the open-source, modular philosophy championed by Edana.
Finally, centralized monitoring of the hybrid model via open monitoring solutions provides a consolidated view of performance, costs, and security. Alerts can automatically trigger scaling actions or load shifts to maintain availability. IT teams thus gain agility and can quickly adapt the architecture to traffic variations, especially during commercial peaks.
Cloud security and compliance
Moving customer and transactional data to the cloud requires strengthened access controls and encryption both in transit and at rest. Centralized Identity and Access Management (IAM) solutions, combined with zero-trust policies, ensure every request is authenticated and authorized. Audit logs and compliance reports are generated automatically to meet regulatory requirements and certification standards.
A Swiss retail chain migrated its e-commerce services to a public cloud while achieving ISO 27001 certification and adhering to PCI-DSS standards. This case illustrates how robust security frameworks in the cloud bolster customer trust and pave the way for new partnerships, all while simplifying internal and external audits.
Locally hosted cloud services in Switzerland also guarantee data sovereignty, valued by both authorities and end customers. This legal and geographic proximity eases compliance with data protection regulations and mitigates risks associated with international data transfers. Retailers can thus reconcile performance with risk management.
Scalability and resilience
Cloud adoption allows real-time resource adjustment based on traffic and marketing campaigns, avoiding overcapacity for most of the year. Serverless architectures and on-demand functions help reduce operational costs and improve development team responsiveness. Pre-production and testing environments can be provisioned and decommissioned automatically, accelerating delivery cycles.
In the event of a major incident, multiple availability zones and automatic failover mechanisms ensure a minimal Recovery Time Objective (RTO). Continuously replicated data across regions enables instantaneous business resumption in case of a failure. This resilience is a key competitive advantage for retailers who cannot tolerate revenue losses or service interruptions during traffic peaks.
Incremental backup mechanisms coupled with automated restoration workflows significantly reduce the risk of irreversible data corruption or loss. IT teams can regularly test their disaster recovery procedures and validate the robustness of business continuity plans. The guarantee of continuous availability thus becomes a decisive selling point.
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Simplify and reduce technical debt
Monolithic architectures concentrate risk and slow down the rapid rollout of enhancements. Reducing technical debt through a phased decomposition and targeted refactoring plan is essential to gain agility and performance.
Breaking down into microservices
Decomposing a monolith into microservices involves gradually isolating key functions (authentication, product catalog, promotions, payments) into independent services. Each service becomes autonomous, testable, and deployable on its own, minimizing side-effects during updates. This modularity also allows scaling each component based on usage and observed performance.
Implementing standardized RESTful APIs or gRPC ensures reliable communication between services while maintaining functional consistency. Each microservice can choose the most suitable technology—be it Node.js for asynchronous processing or Java for transactional robustness. This technical freedom prevents vendor lock-in and aligns the architecture with an open-source, evolving approach.
Automated CI/CD pipelines trigger unit, integration, and end-to-end tests on every change, ensuring consistent code quality. The ability to deploy a single microservice without affecting others accelerates the time-to-market for new features. Teams can experiment, fix, and iterate faster without compromising overall stability.
Refactoring and incremental modernization
Refactoring should follow a pragmatic roadmap that identifies critical areas and quick wins. Start by extracting the most volatile or heavily used components while keeping the monolith operational. This incremental approach limits risks and costs compared to a full rewrite, which is often lengthy and expensive.
Static code analysis and test coverage tools help pinpoint outdated or poorly tested functions. A robust automated test suite ensures that each refactoring maintains functional integrity. With every iteration, technical debt decreases, code readability improves, and new hires can ramp up more quickly on a modernized foundation.
This gradual process also provides the opportunity to introduce new coding standards and best practices, such as dependency injection, clear separation of business layers, and proven design patterns. Ultimately, the codebase becomes more robust, secure, and easier to evolve in support of omnichannel commerce needs.
Proactive management of technical debt
A Swiss online wine retailer launched a technical debt reduction plan after deployment times doubled in less than a year. This example shows that a focused audit followed by targeted refactorings can cut the time needed to introduce new seasonal offers by 40%. The initiative freed teams to focus on product innovation rather than inherited bug fixes.
Regular code reviews, combined with metrics on cyclomatic complexity and test coverage, allow continuous monitoring of debt levels. Technical committees now include a dedicated debt indicator in their dashboard, ensuring each new feature integrates without increasing the backlog. Technical debt thus becomes a natural part of IT governance.
Finally, integrating short, frequent clean-up sprints into the backlog ensures debt doesn’t accumulate. Each sprint allocates capacity to technical tasks, enforcing proactive maintenance and avoiding a snowball effect. This agile debt governance keeps the digital foundation always ready to embrace new innovations.
Build a robust digital foundation for AI and omnichannel
A modernized digital core is the necessary prerequisite for deploying high-performing omnichannel experiences and AI models. It is this foundation that guarantees the reliability of product passports, real-time analytics, and large-scale personalization.
Integration of product passports
Product passports demand granular tracking of each item from manufacture to sale. A centralized PIM (Product Information Management) system brings together technical specifications, traceability data, certifications, and marketing content. A modernized digital core directly incorporates this information into inventory flows and sales channels, ensuring total consistency between product promises and on-the-ground reality.
Orchestrating these passports involves automated update and validation workflows linked to ERP systems and e-commerce platforms. Secure RESTful APIs distribute enriched data to mobile apps, in-store kiosks, and websites, providing accurate real-time information to both customers and staff. This traceability becomes a differentiator, especially amid growing demands for sustainability and transparency.
With a unified repository and rigorous governance, retailers can also offer resale or second-life journeys based on product quality and history. This approach generates new revenue streams, strengthens customer engagement, and aligns with more responsible business strategies.
Real-time analytics
To make analytics truly actionable, you must move beyond batch processing and shift to continuous data streams. Streaming technologies like Apache Kafka or Pulsar deliver the responsiveness needed to instantly detect sales trends, stock outages, or emerging purchase behaviors. A modernized architecture leverages these streams to trigger business alerts and AI scenarios in near real time.
Deploying dynamic dashboards connected directly to data pipelines gives decision-makers an operational and strategic view without delay. Teams can then target promotions on high-value items or adjust supplier orders before stockouts impact revenue. This ability to anticipate and act in real time is a major competitive lever.
Streaming cluster scalability ensures controlled load increases during commercial peaks. Open-source real-time analytics tools can run in cloud-native or on-premises modes, depending on sovereignty and latency requirements, while maintaining usage-based billing. Retailers thus gain a modern decision cockpit ready for next-generation AI use cases.
Omnichannel and personalization
Merging digital and physical channels relies on a centralized platform capable of broadcasting the same customer and inventory data in real time to all touchpoints. Product recommendations, personalized offers, and upsell scenarios then operate on a unified customer profile, regardless of entry channel. This consistent experience strengthens loyalty and increases average order value.
An unnamed Swiss retailer with multiple formats implemented a unified digital core combining CRM, PIM, and e-commerce. It reported a 20% increase in omnichannel revenue and a 15% reduction in product returns. This case demonstrates that dynamic personalization powered by synchronized data transforms customer engagement into a true competitive advantage.
Retailers can tailor promotions based on purchase history, local stock, and seasonality, while respecting customer preferences and consent. This advanced orchestration is the indispensable foundation for generative AI strategies, intelligent chatbots, and truly relevant virtual assistants.
Modernize your digital core for sustainable competitiveness
Digital core modernization rests on three pillars: data unification, cloud migration, and progressive technical debt reduction. Together, they establish an agile, secure platform ready to support AI and omnichannel initiatives. This strategic transformation cuts costs, enhances the customer experience, and positions retailers to anticipate market changes with confidence.
Our Edana experts are available to guide you through renovating your IT architecture, from audit to execution, prioritizing open-source and modular solutions. With our contextual and agile approach, you will gain a sustainable, scalable, high-performance digital foundation.
