Modernizing Analytics and Reports Around Legacy ERP
A pragmatic roadmap for manufacturers and distributors to strengthen data trust, operational visibility, and AI readiness—without disrupting ERP performance
Many manufacturers and distributors continue to run legacy ERP applications because they’re stable, deeply embedded in operations, and too risky (or costly) to replace quickly. Yet business expectations have changed: leaders want faster insight, cross-functional KPIs, self-service reporting, and data foundations that can support advanced analytics and AI.
This article outlines the biggest needs of organizations that continue to use legacy ERPs—and a practical way to address them through an analytics-and-data hub approach that protects ERP performance while modernizing reporting, governance, and data access. Industry research reinforces why this matters now: 70% of digital transformations fall short of their objectives, and data quality and fragmentation are frequent root causes.
At the same time, organizations remain heavily dependent on spreadsheets for “last-mile reporting,” a symptom of siloed data and inconsistent definitions. A recent report highlighted that 90% of organizations still rely on spreadsheets for some of their most vital business data, despite investments in ERP and automation.
For companies that want modernization without disruption, the path forward is clear: extend what works in the ERP and surround it with an architecture that delivers trusted data, consistent KPIs, and scalable analytics. Throughout this paper, we map each need to the capabilities of Silvon Stratum™—a business data hub and analytics platform purpose-built to help manufacturers and distributors unify data, standardize metrics, and enable modern BI tools without overloading the transactional ERP.
Why legacy ERP organizations feel pressure now
Legacy ERPs often excel at what they were designed for: processing orders, managing inventory, running MRP, and executing core financial transactions reliably. But competitive advantage increasingly depends on how quickly the business can detect issues, test scenarios, and align decisions across functions—especially in environments impacted by volatility in demand, supply, labor, and costs.
Meanwhile, many leadership teams are pushing AI and analytics initiatives, only to discover that data challenges block value creation. Deloitte-related CEO research frequently points to data as the main barrier to turning AI into measurable business value. (Note: public summaries commonly cite a “51%” figure, but the clearest primary Deloitte pages accessible publicly emphasize data as a critical barrier without consistently presenting that exact statistic in the excerpted material.)
Against this backdrop, organizations using legacy ERPs typically share nine “big needs.” The remainder of this paper breaks them down and shows how an ERP-adjacent hub approach—implemented pragmatically—can meet them.
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1 – Trusted, accurate data across the business
The most universal requirement is trusted data—not just “more data.” Legacy ERP environments often coexist with CRM, WMS, TMS, EDI platforms, planning tools, quality systems, and spreadsheets. Over time, teams create local definitions and workarounds that fragment truth.
What “good” looks like
- A consistent system of record for reporting (not necessarily the ERP)
- Standard definitions for KPIs and dimensions (customer, product, plant, channel)
- Documented business meaning and lineage for key measures
How Stratum fits
Stratum’s data-hub approach is designed to integrate and harmonize data from multiple sources so teams aren’t forced to reconcile competing numbers across departments. The goal is straightforward: one trusted layer for reporting and analytics, with governed definitions that can be shared across tools (including Excel and Power BI).
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2 – Modern analytics without ERP replacement
Leaders want modern dashboards, drill-down analysis, and flexible reporting—but many legacy ERPs weren’t built for today’s interactive analytics demands. “Ripping and replacing” the ERP is often high-risk and slow, and digital transformation outcomes are uncertain. Recent research has cited that 70% of digital transformations fall short—a caution flag for large, disruptive programs.
What “good” looks like
- Modern analytics delivered outside the ERP
- Faster time-to-value through incremental rollout
- Ability to modernize insights while the core ERP remains stable
How Stratum fits
Stratum enables organizations to preserve their legacy ERP while modernizing analytics through a separate, analytics-optimized environment. This reduces reliance on brittle custom reports and helps avoid forcing the ERP to serve as the analytics engine.
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3 – Self-service access for business users
In legacy ERP environments, reporting commonly becomes an IT queue. Business users submit requests, wait for extracts, and maintain “shadow spreadsheets.” This pattern persists partly because spreadsheets are flexible and familiar. Research continues to show widespread reliance: one recent study reported 90% of organizations rely on spreadsheets for vital business data.
What “good” looks like
- Business-friendly views and guided exploration
- Role-based security and governed access
- A shift from “report requests” to self-service insight
How Stratum fits
Stratum is built to help operational and financial stakeholders explore standardized data safely — reducing IT bottlenecks while keeping governance intact. The platform’s structured approach supports a controlled form of “self-service” that doesn’t devolve into uncontrolled spreadsheet sprawl.
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4 – Integration across systems—without breaking the ERP
ERP data alone can’t answer many of today’s questions:
- Customer performance requires CRM, pricing, and service data
- Supply chain performance requires WMS, transportation, supplier, and quality signals
- Finance needs profitability views that blend cost, price, rebates, and trade spend
Point-to-point integration grows fragile over time; every added connection becomes another point of failure.
What “good” looks like
- A central integration layer that can absorb new sources
- Data harmonization that supports cross-system KPIs
- A future-proof pattern for adding sources without constant rework
How Stratum fits
Stratum is positioned as a unifying data layer—an integration and harmonization hub that can bring ERP and non-ERP data together into consistent models for reporting and analysis.
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5 – Protecting performance and stability of the core ERP
Manufacturers and distributors often operate with tight SLAs around uptime and transaction speed. When reporting workloads hit production databases, performance risks rise—especially during peak periods (month-end, high-volume shipping windows, seasonal demand).
What “good” looks like
- Analytics workload offloaded from ERP transactions
- A read-optimized structure for reporting
- Minimal operational risk when adding new dashboards or users
How Stratum fits
A core advantage of a hub architecture is separation of concerns: the ERP runs the business; the hub runs analytics. Stratum’s design supports this separation, so analytics growth doesn’t compromise ERP stability.
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6 – KPI consistency and operational visibility
Legacy ERP organizations frequently struggle with KPI disputes:
- “Whose margin is correct?”
- “What counts as on-time?”
- “Which inventory number should we trust?”
The problem is rarely math—it’s definitions, timing, data scope, and missing context.
What “good” looks like
- Standard KPI definitions that are adopted across Finance, Ops, and Supply Chain
- Time-series history for trend analysis
- Drill-down capability that explains why the KPI moved
How Stratum fits
Stratum’s manufacturing/distribution analytics orientation supports packaged KPI frameworks and repeatable metric definitions—so teams align around shared, governed KPIs rather than rebuilding calculations in every report.
Download Silvon’s KPI Guide:
Defining & Delivering Effective Key Performance Indicators
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7 – Practical data governance without heavy overhead
Many governance initiatives fail because they become bureaucratic or disconnected from daily decisions. But without governance, self-service becomes chaos.
Governance needs to be right-sized:
- Secure access control
- Consistent metric definitions
- Clear ownership of data domains
- Shared glossary/definitions so users interpret data correctly
What “good” looks like
- “Lightweight governance” embedded into tools and workflows
- A shared vocabulary and definitions for metrics and attributes
- Auditability and controlled access where needed
How Stratum fits
Stratum’s approach supports governed definitions and controlled access while still enabling broad consumption. This is especially important when business users consume data through familiar tools like Excel and Power BI.
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8 – AI and advanced analytics readiness (even before AI deployment)
Organizations often jump to AI tooling before fixing foundational data problems. Deloitte-oriented executive research and commentary consistently emphasizes that data issues constrain AI value.
AI readiness requires:
- Clean, consistent, well-modeled data
- Adequate history and context
- Stable definitions and feature-ready datasets
What “good” looks like
- Curated, analytics-ready datasets that don’t change unpredictably
- Clear lineage and definitions to support model trust and explainability
- Scalable data pipelines that can incorporate new signals
How Stratum fits
A data hub provides the “AI runway” by improving data quality, consistency, and availability across domains. Stratum’s positioning as a central harmonization and reporting layer aligns to this readiness requirement—without forcing AI ambitions to depend on fragile spreadsheets and ad hoc extracts.
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9 – Cost control and ROI justification
Legacy ERP organizations are usually pragmatic: they want improvements that pay back quickly, reduce risk, and avoid multi-year disruption. They also know transformation success is not guaranteed—again, a reality underscored by widely cited transformation outcomes research.
What “good” looks like
- Incremental modernization with clear ROI milestones
- Reduced operational risk (no “big bang”)
- Reuse of existing ERP investments while modernizing insight
How Stratum fits
Stratum’s value proposition aligns well to ROI-driven modernization: implement a hub, standardize metrics, enable modern reporting, then expand subject areas and departments over time — delivering measurable value earlier than a full ERP replacement would.
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A pragmatic roadmap: modernize around the ERP
A successful modernization approach for legacy ERP organizations typically follows four phases:
Phase 1: Align on priority outcomes and KPI definitions
Start with the decisions the business wants to improve (service levels, inventory, margin, sourcing performance). Then standardize KPI definitions and the dimensional model needed to support them.
Phase 2: Build a unified data foundation
Employ a data hub to integrate ERP and the most critical adjacent sources (CRM, WMS, planning, external reference data). Establish common dimensions and reconcile inconsistencies.
Phase 3: Deliver modern analytics through familiar tools
Enable on-line analysis, Power BI and Excel reporting on top of the governed data hub so business users get self-service insight without unmanaged extracts.
Phase 4: Expand, optimize, and prepare for AI
Broaden subject areas (customer profitability, demand planning, supplier scorecards), improve data quality continuously, and curate datasets for predictive models.
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Conclusion: Extend, don’t disrupt
Companies running legacy ERPs aren’t “behind.” They’re often choosing stability and continuity while seeking modernization that is practical, low-risk, and fast to value. The biggest needs are consistent across industries—trusted data, modern analytics, self-service access, integration, ERP performance protection, KPI alignment, right-sized governance, AI readiness, and ROI-driven progress.
A business data hub approach—implemented incrementally—directly addresses these needs. It modernizes reporting and analytics around the ERP rather than forcing a risky replacement. For manufacturers and distributors, this approach is especially compelling because it aligns operational and financial truth across the end-to-end value chain.
Stratum is designed for this exact scenario: unify and govern data from legacy ERPs and adjacent systems, standardize KPIs, enable modern BI tools, and create a durable foundation for advanced analytics—while protecting the stability of the system that runs the business.