Build Your ERP Analytics Roadmap: A 3-Year Plan for Turning Data into Results

Your ERP system collects everything: orders, invoices, inventory, and forecasts. But how much of that data is actually driving faster decisions?

ERP data is supposed to improve speed and accuracy, yet the average month-end close still takes eight days. According to the latest SAPInsider benchmark report, 82% of finance teams identified manual steps as the main bottleneck. This reflects more than a workflow issue. It points to a broader gap between ERP investment and analytics maturity.

This guide is built for IT directors and data analysts who are ready to change that. You’ll learn how to build a 3-year ERP analytics roadmap that turns disconnected reports into decision-ready dashboards and predictive models. From aligning with business goals to choosing tools and driving adoption, every section delivers practical steps to help you get results.

If your ERP system feels like a black box of buried potential, this roadmap will help you unlock it.

Phase 1: Laying the Foundation

Every successful ERP analytics roadmap begins with clarity. Before choosing tools or building dashboards, organizations need to define why analytics matters, what success looks like, and how to secure long-term support. Skipping this step often leads to fragmented metrics, underused platforms, and ERP implementation delays.

Define Business Objectives

Your ERP system is only as valuable as the decisions it improves. Instead of defaulting to standard reports, start by identifying the strategic goals that matter most, such as margin improvement, faster close cycles, tighter inventory management, or better supply chain coordination. Tie each goal to a measurable KPI and link it to a specific business process.

For example, a manufacturing company may aim to cut carrying costs by improving forecast accuracy within its enterprise resource planning system. That single objective spans sales, inventory, finance, and production planning. Clarifying these dependencies ensures that analytics priorities support the broader business strategy, not just departmental reporting.

Avoid building dashboards for the sake of visual appeal. Focus on delivering data that supports decision-making across the business functions that drive real outcomes.

Audit the Current Reporting Landscape

A typical ERP implementation spawns dozens of reports. Some are useful, but many are forgotten. Start your roadmap with a full audit of the ERP analytics environment, including native reports, spreadsheets, third-party tools, and shadow systems.

This step uncovers duplication, manual workarounds, and inconsistencies across the reporting structure. For instance, two departments might calculate cost-of-goods-sold differently using the same ERP software. Identifying these conflicts early helps your project team design a unified analytics model that prevents confusion later in the implementation process.

Don’t ignore user behavior. Track which reports are actually used and who relies on them. This insight will inform which data elements, metrics, and tools are essential for a successful ERP analytics strategy.

Get Executive Buy-In

Analytics projects fail without executive support. To secure lasting sponsorship, position your ERP analytics roadmap as a way to achieve business priorities.

Start with one or two quick wins that align with the current ERP system’s capabilities. Examples include automating financial reporting or providing real-time data visibility into order fulfillment. Frame these wins as part of a broader ERP transformation, not isolated improvements.

Demonstrated progress and business relevance increase the likelihood that executives will champion the roadmap, protect the budget, and remove roadblocks. This step is especially critical in organizations preparing for a cloud ERP migration or expanding their ERP platform to support analytics at scale.

For organizations looking to align analytics strategy with business goals from day one, RubinBrown’s ERP Advisory Services can help define success metrics, build executive alignment, and accelerate roadmap development.

Phase 2: Infrastructure and Data Readiness

Analytics efforts fail without a solid data foundation. This phase ensures that your ERP analytics roadmap rests on infrastructure that scales, aligns, and delivers. The right setup supports accurate reporting today and advanced analytics tomorrow.

Data Warehousing Basics

ERP systems are optimized for transactions, not analysis. A centralized data warehouse creates a consistent, query-ready layer where historical, cross-functional, and high-volume data can be accessed without performance drag.

Select an architecture that aligns with your ERP implementation strategy and anticipated growth. The warehouse must support rising data volumes, increased user access, and changing business structures. It becomes the foundation for reliable data analytics and long-term ERP success.

Without this layer, every dashboard or model built later will suffer from slow performance, inconsistent data, or excessive manual effort.

Integration and ETL Planning

ERP data only tells part of the story. Integrating surrounding systems in finance, supply chain, operations, and CRM unlocks the full business context behind your metrics.

Design your extract, transform, load (ETL) process around real operational cycles. Some business processes demand hourly updates; others only require daily refreshes. Define data sources, business logic, refresh intervals, and dependencies in advance. This avoids future slowdowns during ERP implementation or when adding new systems to your analytics stack.

Proper integration ensures that your analytics reflect how the business actually runs. Not just how your ERP system stores transactions.

Data Governance Strategy

Analytics without governance leads to misalignment, conflicting metrics, and failed adoption. As your ERP system expands, governance protects consistency across departments, tools, and users.

Assign ownership for major data domains, including finance, inventory, customer information, and establish rules for access, quality checks, and change controls. Governance should be lightweight but explicit. It should support agility while creating a shared understanding of what each metric means.

A strong governance model reduces friction during future implementation phases, especially as you introduce new users, analytics use cases, or cloud-based ERP extensions.

Phase 3: Choosing the Right Analytics Tools

Tools shape how users interact with ERP data. The right analytics platform improves adoption, reduces friction, and shortens the path from data to decision. The wrong choice adds complexity, frustrates users, and inflates the ERP implementation timeline.

This phase ensures your analytics roadmap is supported by systems that align with your business needs and technical strategy.

Criteria for BI Tool Evaluation

No analytics solution is one-size-fits-all. Start by defining the core requirements based on your current ERP system, business processes, and stakeholder roles. Business users need intuitive dashboards and self-service capabilities. Analysts require access to raw data, flexible modeling, and the ability to support cross-functional reporting.

Evaluate each platform on scalability, ease of use, data governance features, and integration with your ERP architecture. Go-live is only the baseline. Real success comes when teams apply ERP insights to make faster, more accurate decisions.

Run structured pilots using real ERP data. Engage a cross-functional project team that includes IT, finance, operations, and line-of-business leaders. Their feedback will reveal whether the tool supports your workflows or adds new roadblocks.

Build vs. Buy Considerations

Custom ERP analytics solutions offer flexibility and domain-specific modeling. They allow organizations to define metrics and workflows that match how the business actually operates. But they require internal expertise and ongoing investment in development, support, and data quality controls.

Off-the-shelf tools deliver speed, vendor support, and rapid deployment, especially in cloud ERP environments. However, these tools may force compromises in how business requirements are met.

Many enterprises find that a hybrid model works best. Standard dashboards handle core reporting needs, while custom ERP solutions address advanced analytics, supply chain optimization, or predictive forecasting.

No matter the mix, the key is alignment with business strategy, data architecture, and long-term ERP transformation goals. Choosing the right ERP system without the right analytics capability leaves ROI on the table.

Phase 4: Building a Phased Implementation Roadmap

A successful ERP analytics roadmap depends on pacing. Trying to deploy everything at once leads to failure. A phased implementation approach gives your project team the room to iterate, validate, and scale without overwhelming business operations. This phase aligns the analytics rollout with enterprise readiness and ensures the ERP system delivers value at every stage.

12–36 Month Timeline: Crawl, Walk, Run

In the first 6–12 months, focus on replacing spreadsheet-based reporting with standardized dashboards that draw directly from your ERP platform. This “crawl” phase ensures consistent definitions and centralized metrics. It also builds trust in the data.

From months 12–24, enable self-service analytics for key departments. Business users should be able to explore real-time KPIs without routing everything through analysts. This “walk” phase expands access while enforcing governance and accuracy.

By the third year, you enter the “run” phase. The analytics roadmap now supports advanced capabilities like predictive modeling, anomaly detection, and scenario planning. These tools embed analytics into daily decision-making and unlock the full benefits of ERP transformation.

Team Roles and Ownership

No ERP implementation succeeds without clear accountability. Define responsibilities early and enforce them throughout the project lifecycle.

• Data engineers own pipelines, warehousing, and ERP system integration.

• Data analysts translate requirements into usable models and dashboards.

• Business users provide input, validate outputs, and use insights to drive action.

Document workflows and escalation paths. Your implementation team must balance technical execution with business strategy alignment. A strong handoff plan between IT and line-of-business owners is essential for long-term ERP success.

Cross-functional clarity is one of the most overlooked success factors in ERP analytics. Without it, even the best tools and infrastructure will fail to deliver.

Phase 5: Optimization and Scale

With the core analytics roadmap in place, your ERP strategy must shift from project mode to performance mode. Optimization ensures that ERP analytics continues to evolve as your business, data volume, and market pressures change. This phase is about scale, consistency, and long-term success.

Establish an Analytics Center of Excellence

A well-run ERP analytics program cannot rely on ad hoc effort. Establishing a Center of Excellence (CoE) helps formalize best practices across data modeling, reporting standards, and dashboard development. This group supports teams with reusable templates, governed metrics, and implementation guidance.

The CoE should not centralize all work. Its role is to enable faster execution across departments without sacrificing consistency. It reinforces collaboration by aligning business units, analysts, and IT stakeholders under a unified ERP solution.

For organizations implementing a custom ERP system or hybrid cloud ERP architecture, the CoE becomes a critical support structure as analytics workloads scale across new business units or geographies.

Monitor ROI and Adjust KPIs

Ongoing measurement keeps your analytics strategy tied to results. Track performance against the business objectives defined in Phase 1, whether that’s reduced close time, improved forecast accuracy, or better inventory visibility. Use these metrics to assess value and surface areas for iteration.

Review performance quarterly. Use this cadence to refine your ERP implementation roadmap, reprioritize analytics projects, and validate the relevance of KPIs as business needs evolve.

Optimization isn’t about perfection. It’s about creating a continuous improvement loop that adapts to change, supports ERP adoption, and helps the entire ERP system support strategic decision-making long after go-live.

To support long-term analytics maturity and evolving business needs, many organizations turn to RubinBrown’s ERP Implementation Experts for help refining KPIs, adjusting architecture, and scaling adoption across business units.

Real Results: Scaling ERP Analytics After Go‑Live

RubinBrown worked with a $1.1 billion construction and engineering firm that struggled with fragmented reporting after years of growth and system sprawl. The engagement focused on aligning ERP selection and implementation decisions with long-term analytics goals, including standardized KPIs, consistent financial reporting, and scalable data structures.

As the ERP environment stabilized, the client moved into optimization by consolidating reporting, improving data reliability, and establishing a foundation for analytics maturity across finance and operations. This approach enabled leadership to track performance consistently, shorten decision cycles, and support future analytics expansion as the business grew.

Read the full story on how RubinBrown helped this construction firm build an ERP foundation that supports scalable analytics and decision-making

Turn ERP Data into a Scalable Analytics Strategy

An ERP system by itself does not generate business insight. To get results, you need a structured analytics roadmap that connects strategy, architecture, and execution.

This phased approach helps your team move from basic reports to predictive capabilities with clear direction at every stage. It keeps analytics tied to business goals while reducing risk and building trust in the data.

If you want to turn ERP data into something actionable, timing matters. Companies that invest in analytics maturity now will outperform those still struggling with spreadsheets next year. Book a Data Strategy Workshop with RubinBrown to create a roadmap that delivers measurable value.