Beyond ERP: Achieving the Maximum Potential of Your ERP Data

In our previous blog, we explored why despite heavily investing in Enterprise Resource Planning (ERP) systems, we 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 system of records 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. The foundational steps are necessary not just to manage the data but also to ensure building right data foundation that is scalable and repeatable to help enterprise finance implement the 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 be aligned with strategic goals and provide insights into performance, efficiency, and growth. Most organizations would already have defined KPIs and Metrics to track across their different verticals & departments. Typically organizations do these Metric reporting either through Excel or Powerpoint on a monthly basis. The management therefore is always looking at the rear view of the business. The root cause for this challenge comes because of the manual effort in gathering data for these KPIs.

Hence, to start the automation of near real time reporting of these organizations, we recommend starting with identifying the most pressing and challenging issues that your team faces and finalizing the KPIs associated with these issues. E.g. If M&A integration is a big challenge in reporting the metrics then start with that objective or it can be as simple as identifying metrics that matter and reporting them. It is important that the most pressing challenge should be leveraged as an achievable project while accommodating the unique challenges and constraints of an existing business environment. The transformation of this problem development at the beginning of a multi-pronged program increases awareness of the importance of data and provides a foundation for creating value out of your challenges.

Steps to identify the focus area:

1. Identify Business Objectives: Identify which area would benefit most from such programs and then define the KPIs with the organization’s strategic objectives as well as RoI for the program. If KPIs are already defined, then identify the area that is most challenging in reporting automated KPIs and metrics.
2. Consult Stakeholders: Gather input from various departments to understand their data needs and priorities.
3. Select Actionable Insights: Choose the KPIs that provide actionable insights and can drive decision-making.
4. Establish Benchmarks: Define benchmarks and targets for each KPI to measure performance.
5. Regular Review: Continuously review and update KPIs to ensure they remain relevant.

 

Example: A global manufacturing company wanted to automate their monthly management reporting. They identified various Metrics that matter across Revenue, Cost and Cash Flow that will be important for their management. With continuous focus on these KPIs, they could closely monitor their financial performance and profitability on real time, leading to targeted strategies for improvement.

 

Blending Data Across Systems with a Robust Data Model

A robust finance data model is crucial for unifying data across different systems. It defines how data is structured, related, and stored, ensuring consistency and accuracy. A well-designed data model allows organizations to perform complex analyses and generate meaningful insights. CFO teams focus more on accuracy and reliability than creativity. Hence, it is imperative that a robust Data model is defined and implemented. In our experience, most challenges for cost overruns and multi-year Data & Analytics programs for CFOs happen because this step is avoided. No doubt this is also the most challenging aspect. It is better to talk with firms, such as Midoffice Data, who have ready-made and configurable industry data models for Corporate Finance. This will improve the success rate of the program as well as reduce time to market.

 Steps to Create a Robust Data Model:

1. Conduct Data Audit: Review existing data sources to understand the structure, formats, and relationships.
2. Design Data Schema: Create a schema that outlines the entities, attributes, and relationships. Ensure it supports the current KPI needs but is also scalable for future data needs.
3. Standardize Naming Conventions: One of the most common pitfalls in data integration is inconsistent naming conventions across systems. For example, a customer might be labelled as “ClientID” in your ERP and “CustomerID” in your CRM. Establishing a standard naming convention across all systems is a simple step that can save countless hours of reconciliation work down the road.
4. Build and Test the Model: Construct the data model in a staging environment and test it with real data to identify and rectify issues.
5. Deploy and Maintain: Implement the data model in the production environment and establish a maintenance plan to update it as needed.

Example: A retail chain faced issues reconciling sales data from their POS system with inventory data from their ERP due to different product codes. By implementing a unified data model and standardizing naming conventions, they were able to accurately track inventory levels and optimize stock management. This helped the retail chain to control inventory costs & improve their profitability.

 

Integrating ERP, Other Applications, and Offline Files

A data foundation that is scalable and repeatable starts with integrating it with other business applications and offline files. This involves creating a seamless flow of information between your ERP, CRM systems, procurement systems and other data platforms, and even spreadsheets or CSV files. ETL (Extract, Transform, Load) tools or data integration software can help automate this process, ensuring that data from various sources is consistently and accurately combined.

Steps to Integrate Data:

1. Assess Data Sources: Identify all relevant data sources, including ERP systems, CRM, HRM, SCM, marketing tools, and offline files.
2. Choose the data Integration Tools: The data integration tools help in extracting data from various sources, transforming it into a suitable format, and loading it into a centralized data warehouse.
3. Data Mapping and Transformation: Establish data mapping rules to standardize and transform data into a consistent format. This involves defining how data fields from different sources correlate with each other. Building scalable & flexible transformation rules are the key to make the data models futuristic & widely useable.   
4. Implement Data Pipeline: Set up automated data pipelines to regularly update the centralized data repository with fresh data from all sources.
5. Focus on Simplicity: When setting up your data integration process, focus on simplicity. Start with integrating the most critical data sources first—such as sales and finance—before moving on to others. Simple, consistent data flows are easier to manage and scale over time.

Our blog series on Lakehouse Architecture provides snippets on best practices to setup the data foundation.

Example: A global manufacturing company integrated their SAP ERP with Salesforce (CRM), Project management software, and various internal Excel files used by different departments to automate the manual report creation for raising invoices to their customers. By automating data flow between these systems, they reduced manual data entry errors and created a single source of truth for their operational data.

Conclusion

Achieving the maximum potential of your ERP data requires a holistic approach that includes integrating various data sources, developing a robust data model, visualizing data effectively, selecting meaningful KPIs, and leveraging advanced technologies like GenAI. By adopting these strategies, organizations can transform their data into a powerful asset that drives informed decision-making and strategic growth.

 

Additional Case Studies from Industry Leaders:

• General Electric (GE): GE integrated their ERP with various data sources and used a robust data model to streamline their financial reporting processes. This integration allowed them to gain real-time insights into their operations, improve financial accuracy, and enhance strategic planning. Link
• Procter & Gamble (P&G): P&G blended data from their ERP, supply chain systems, and market research tools using a unified data model. This holistic view enabled them to optimize inventory management, reduce costs, and better understand market trends. Link

By learning from these industry leaders and implementing similar strategies, your organization can unlock the true potential of its ERP data, leading to more efficient operations and a competitive edge in the market.

 

We at Midoffice Data work with enterprises to help them navigate the complexities of ERP systems by harnessing their data to achieve strategic objectives. By leveraging a data led approach that aligns to using the appropriate technology additions, organizations can turn their ERP data into a powerful asset for operational and financial insight and to drive enduring benefits.