Data integrity is a defining success factor for modern ERP programs. As organizations accelerate their digital initiatives, expand data estates, and adopt cloud analytics platforms, ensuring operational, financial, and customer information has become a strategic differentiator. Without reliable data, the promises of automation, AI-driven insights, predictive operations, and cross-functional reporting collapse.
Across the manufacturing, distribution, and service sectors, organizations are coming to the same simple truth: ERP transformation succeeds when the underlying data foundation is coherent, accessible, and governed.
Today’s data integrity agenda goes beyond cleansing master records. Enterprises must modernize how data is connected, retained, and distributed across business units without resorting to expensive rip-and-replace ERP initiatives. The effects, when done right, go well beyond the datasets.
Silos, Paper Processes, ‘Hidden Factory’
The data integrity challenge is most visible on the shop floor. Despite decades of ERP investment, many industrial organizations still rely on manual processes that are paper-based and siloed. SAPinsider research shared during a manufacturing webinar revealed 79% of factory operators still depend on handwritten checklists or offline workflows. This fragmentation produces the “Hidden Factory,” which consists of non-value-added activities, delays, data entry errors, and inconsistent reporting that erodes profitability.
Experts from CAI Software and Hubbell Incorporated emphasized during the webcast that while manufacturers have a lot of data, the information is often unstructured or untrustworthy. As a result, supervisors are managing symptoms rather than root causes. Without real-time overall equipment effectiveness (OEE), downtime analysis, or reliable work-center metrics, organizations reinforce reactive firefighting instead of being proactive.
Creating trustworthy data starts with Stage 1 digital foundations focused on visibility and accurate real-time capture to establish consistent reporting across the platform. The goal is to ensure everyone operates from a single source of truth based on fact-driven decision-making.
Governing Data Without ‘Replatforming’
Large enterprises with diversified system landscapes face an even more complex integrity challenge. A US-based steel manufacturer recently confronted this reality as it attempted to harmonize data across multiple systems. Rather than undertake a system overhaul, the company adopted CData Virtuality as a semantic layer to unify data access across cloud, on-premises, and application sources.
By virtualizing data instead of physically moving it, the organization eliminated months-long integration cycles. Reporting workflows that previously depended on manual extracts and case-by-case data preparation were automated. Finance gained consistent cross-divisional reporting. Sales regained trust in historical performance data. Production bonus calculations became real-time. AI-driven safety applications began receiving accurate inputs. Most importantly, the executive team gained unified visibility into global performance for the first time.
This transformation required more than new technology; it demanded a cultural reset. Through collaborative design workshops and executive alignment efforts, teams embraced a shared view of data as a strategic asset. The manufacturer established a future-ready foundation capable of supporting new divisions, real-time streaming, and change data capture enhancements without altering core ERP systems.
Integrity Through Managed Data
Data integrity also suffers when organizations hold onto legacy ERP landscapes longer than they should. Enterprises often maintain decades-old SAP and non-SAP systems solely to preserve historical records, but these platforms become expensive, insecure, and difficult to audit. Maintaining this legacy is important because it has sensitive historical data, but they also must adhere to modern data retention and privacy regulations. Centralizing the historical data from these systems through decommissioning into a modern, managed archive simplifies governance and ensures compliance.
Successful decommissioning follows a methodical, three-phase approach that provides a structured path forward.
- Phase 1: Analysis and retention definition. This involves analyzing data within the legacy system to define clear retention policies. It precisely identifies the data that must be preserved to meet specific requirements, which helps companies avoid unnecessary migration of obsolete information.
- Phase 2: Data extraction and consolidation. The platform connects to legacy applications and other sources to extract the required data, documents, and their original business context. This information is then consolidated into a single, secure, and accessible historical data archive to help ensure continuity.
- Phase 3: System decommissioning. Once all necessary historical data is securely archived and validated, the organization can decommission the legacy system and terminate the associated software licenses. This action ends any associated costs and eliminates the security and compliance risks related to the old system.
What This Means for ERP Insiders
Integrity drives measurable efficiency gains. Technology leaders will see day-to-day workflows shift from manual data preparation toward automated, self-service access models. Companies such as the steel manufacturer demonstrate ROI measured in faster reporting cycles, eliminated rework, and months-to-minutes integration acceleration.
Market momentum favors unified data layers. As semantic layers, virtualization platforms, and decommissioning solutions gain traction, ERP professionals will increasingly operate in hybrid environments spanning SAP, Oracle, Microsoft, and cloud data platforms. Daily responsibilities will include evaluating providers based on governance controls, integration flexibility, metadata transparency, and real-time performance.
Adoption success requires disciplined implementation. Case studies show how cultural alignment, iterative workshops, and operator-to-executive visibility are critical for trustworthy data operations. Expect to prioritize cross-functional governance bodies, invest in foundational data models, and lead structured decommissioning programs that reduce technical debt while modernizing reporting and analytics capabilities.
