In our previous
blog,
we explored why despite heavily investing in Enterprise Resource Planning (ERP) systems, organizations still struggle to get basic financial insights. In this blog, we delve into a detailed approach on how organizations can maximize the potential of their ERP, other systems, and tribal knowledge data by integrating various systems of record and creating robust data models.
The approach mentioned in this blog ensures that enterprises can set up a robust data foundation for deriving actionable insights from their data. These foundational steps are necessary not just to manage data, but also to build a scalable and repeatable data foundation that helps enterprise finance implement AI use cases with confidence and accuracy.
Selecting the Problem to Solve or KPIs that Matter
Choosing the right KPIs is essential for supporting different parts of the organization with financial information. KPIs should align with strategic goals and provide insights into performance, efficiency, and growth.
Most organizations already define KPIs and metrics across verticals and departments. Typically, these metrics are reported via Excel or PowerPoint on a monthly basis. As a result, management is often looking at the rear view of the business. The root cause of this challenge is the manual effort required to gather data for these KPIs.
To begin automating near real-time reporting, we recommend identifying the most pressing and challenging issues your team faces and finalizing the KPIs associated with those issues. For example, if M&A integration is a major reporting challenge, start with that objective. It is important that the most pressing challenge is structured as an achievable project while accommodating existing business constraints. Addressing this early increases awareness of data importance and builds a strong value foundation.
Steps to Identify the Focus Area:
- Identify Business Objectives: Determine which area would benefit most and define KPIs aligned with strategic objectives and ROI. If KPIs already exist, identify the most challenging reporting area.
- Consult Stakeholders: Gather input from departments to understand data needs and priorities.
- Select Actionable Insights: Choose KPIs that drive measurable decision-making.
- Establish Benchmarks: Define targets to measure performance.
- Regular Review: Continuously review and refine KPIs to ensure relevance.
Example: A global manufacturing company automated monthly management reporting by identifying key revenue, cost, and cash flow metrics. With continuous KPI monitoring, they achieved real-time financial visibility and improved profitability strategies.
Blending Data Across Systems with a Robust Data Model
A robust finance data model is crucial for unifying data across systems. It defines how data is structured, related, and stored, ensuring consistency and accuracy. CFO teams prioritize accuracy and reliability. Many cost overruns in multi-year data programs occur because this foundational step is skipped.
It is advisable to work with experienced firms such as Midoffice Data that offer configurable industry finance data models. This improves program success rates and reduces time to market.
Steps to Create a Robust Data Model:
- Conduct Data Audit: Review existing data sources and relationships.
- Design Data Schema: Outline entities, attributes, and relationships while ensuring scalability.
- Standardize Naming Conventions: Align naming across systems (e.g., CustomerID vs ClientID).
- Build and Test the Model: Test in staging before production deployment.
- Deploy and Maintain: Implement in production and establish governance processes.
Example: A retail chain reconciled POS and ERP inventory data by implementing a unified data model and standardized naming conventions. This improved inventory control and profitability.
Integrating ERP, Other Applications, and Offline Files
A scalable data foundation integrates ERP, CRM, procurement systems, and offline files such as spreadsheets. ETL tools help automate data extraction, transformation, and loading into a centralized warehouse.
Steps to Integrate Data:
- Assess Data Sources: Identify ERP, CRM, HRM, SCM, marketing tools, and offline data.
- Choose Data Integration Tools: Select tools to extract, transform, and load data into a warehouse.
- Data Mapping and Transformation: Define transformation rules for consistency and scalability.
- Implement Data Pipeline: Set up automated refresh pipelines.
- Focus on Simplicity: Start with critical systems like finance and sales before expanding.
Our
blog
series on Lakehouse Architecture provides best practices for setting up a strong data foundation.
Example: A global manufacturing company integrated SAP ERP with Salesforce CRM, project management tools, and Excel-based reporting. Automating these data flows reduced manual errors and created a single source of truth.
Conclusion
Achieving the maximum potential of ERP data requires a holistic approach that includes integrating data sources, developing robust models, visualizing effectively, selecting meaningful KPIs, and leveraging technologies such as GenAI. These strategies transform data into a strategic asset that drives informed decision-making and sustainable growth.
Additional Case Studies from Industry Leaders
- General Electric (GE): GE integrated ERP with multiple data sources and leveraged a lakehouse architecture to improve financial reporting and strategic planning.
Link - Procter & Gamble (P&G): P&G unified ERP, supply chain, and market data to optimize inventory and reduce costs.
Link
By implementing similar strategies, organizations can unlock ERP data value and gain a competitive advantage.
At Midoffice Data, we work with enterprises to navigate ERP complexity through a data-led approach aligned with the right technology stack. This enables operational clarity, financial insight, and long-term value creation.