ERP data consolidation: How to unify financial data for better, streamlined consolidated reporting

Enterprise resource planning (ERP) systems are integral to helping companies manage their  day-to-day business activities, including accounting, project management, risk management, and compliance. 

They provide a cohesive framework for managing human resources, supply chains, and customer relationships and can be integrated with other key business systems, such as budgeting software and financial reporting systems, to significantly extend their capabilities. 

Almost every company out there has an ERP and in fact, many operate with multiple ERPs. For a company working with multiple different ERPs, each is like a different instrument in an orchestra that contributes its own unique sound to the performance. However, they all must play in perfect sync to create the harmonious sound that only an orchestra can create.   

Similarly, different ERPs must work together to prevent data fragmentation and duplication across the different systems, which is critical to ensuring the company has accurate and comprehensive data to work with.

The problem is, consolidating data from multiple ERPs can be pretty challenging due to varying formats, security concerns, discrepancies, and a lack of standardization. In this article, we will look at how to consolidate data across multiple ERPs, the challenges you can expect to encounter, and how to address them.

What is ERP data consolidation?

ERPs merge data from various processes and departments, such as accounts payable, procurement, customer relations, and employee data, into one cohesive system. They  streamline workflows, enhance data accuracy, and reduce operational redundancies to improve business agility.

SaaS businesses today often operate across different regions or countries, often as a result of mergers and acquisitions. In these situations, they often tailor their tools and processes per specific geographic or functional needs and as a result, end up using multiple ERPs. These might be different instances of the same ERP software (e.g. NetSuite) or different ERPs altogether (e.g. NetSuite + Oracle, + SAP, etc.). 

In either case, maintaining multiple ERPs across the entire business will create data silos and inconsistencies if the data they contain are not consolidated into one central system. With ERP data consolidation, CXOs gain greater visibility into the business performance, reporting, and analytics.

Scenarios that require consolidation

There are two scenarios in which ERP data consolidation would be required.

Different ERPs

In a perfect world, every business would use the same ERP software configured exactly the same for every one of its locations. However, in the world of business, there are plenty of reasons why different ERP systems might be necessary. Here are a few of the most common ones:

• Historical mergers and acquisitions: Many businesses, especially in the SaaS industry, undergo mergers and acquisitions to meet geographical and service-based expansion needs. Often, the new businesses acquired are already using different ERPs. Immediately integrating systems is difficult and costly, so businesses continue using multiple software until they have a well-defined data consolidation strategy.

• Department or region-based systems: ERP systems may differ across departments depending on their workflows or across regional offices based on localized business processes. Compliance needs may also vary based on specific business activities and/or geography requiring specific features only available in different ERP solutions. 

• Rapid growth and expansion: As companies grow and expand to tap global markets or cater to clients from different industries, they usually prioritize service delivery over standardization. Given this, they may have to adopt different ERPs to meet client needs in specific regions or business functions. 

Multiple instances of the same ERP 

Multiple-instance ERP systems use the same ERP but have separate ERP installations for different business units, regions, or subsidiaries.

This situation can arise from many of the same reasons noted above for businesses that choose to use different ERPs.   

Using multiple instances of the same ERP system decentralizes data management, allowing each system to operate independently, which in turn, allows for customization. This approach caters to specific regional or functional requirements, making it easier to comply with local regulations and manage diverse business needs. 

Why ERP data consolidation is necessary and the benefits it provides

Either of the scenarios described above can lead to fragmented and siloed data which can negatively impact the business as a whole. 

Consolidating data across multiple ERPs provides companies with a single source of truth across the entire organization is critical to accurate financial analysis and reporting and helps businesses more effectively track their KPIs, monitor budgets, and create accurate forecasts. 

Companies benefit from ERP data consolidation in the following ways:

• Streamlined processes: Centralizing data from multiple ERPs helps streamline processes and reduces manual efforts. It eliminates redundant processes and saves time—improving productivity and resource utilization. It also facilitates regular audits to help ensure data integrity.
• Improved data accuracy and security: Using data from varied systems increases the risk of errors and discrepancies. Consolidating data minimizes the chances of inaccuracy and ensures secure data usage in compliance with regulations, such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA).
• Better decision-making: With a single source of truth for all the different regions and/or entities or subsidiaries of a business, its leaders can access more reliable insights faster for better, data-driven decisions.

The important role of local charts of accounts in ERP data consolidation

The chart of accounts is a critical component of an ERP and one of the most important aspects of consolidating different ERPs. 

Local charts of accounts are used to comply with local accounting requirements. They provide the necessary detail for statutory financial statements and local taxable income statements and are used for financial reporting and analysis. However, for companies using multiple ERPs, it’s not uncommon for the chart of accounts (COA) is each to differ based on local or regional regulations, business practices, and reporting requirements. These variations lead to differences in how financial data is categorized and reported for different regions, entities, subsidiaries, etc.

For the purposes of data consolidation, the accounting principles and structure of the parent company play a key role. Financial information in the consolidated accounts usually differs from that in the statutory financials because of:

• Differences between local and global accounting standards
• Mismatch in data formats and structures due to varied accounting practices
• Incompatibility of systems used for local accounting and global financial reporting

Companies operating across multiple countries must consolidate local charts of accounts from all their subsidiaries/regions with their global chart of accounts, which begins with a process called data mapping. 

Data mapping local COA ensures that financial entries from different regions are accurately translated into a unified master dataset. For example, what one region records as “Sales Revenue” might be classified under a different account in another region. Therefore, each line item in the local COA must be carefully mapped to the master dataset to maintain consistency and accuracy in consolidated financial statements. 

Now, let’s take a closer look at the actual mechanics of consolidating ERPs.

How to consolidate data across multiple ERP systems

Consolidating data from multiple ERPs requires careful planning and coordination. It is typically a two-step process and starts with mapping the COA.

Step 1: Data mapping:  

The objective of data mapping is to ensure that the receiving platform knows how to interpret the data you are bringing in from the other systems, so that nothing is left out during the import process. 

This is necessary because the two systems likely have different names or codes for the same type of data. If you don't map (or translate) the data you're bringing into one system from another, the receiving system won’t know what to do with the data and may leave it out of the import.

Data mapping is pretty simple in concept, though enormously time-consuming depending on how many systems you're mapping and the sheer volume of the data. It requires that you look at the data in each and every data field of one ERP to find its corresponding destination in the target ERP. This process must be repeated for every ERP you’re using, whether the ERP software is the same or different. 

Using Excel templates can help you structure the data so the receiving system can properly integrate it. 

In our simplified example below, we have two ERPs (System A and System B). These could be either two instances of the same ERP, such as NetSuite, or different ERPs like NetSuite and Oracle for example. The same process applies. 

Our goal is to consolidate the charts of accounts from both systems into a third ERP so we can create consolidated financial statements. 

To do this, we first must decide which data set will be the "master" dataset. In this case, the master data set is a third instance of the ERP, which is represented by the FP&A Mapping column. Then you go through each data set, one-by-one, mapping the fields in the dataset to the equivalent field in the "master" data set.

If this seems deceptively simple, that’s because it is. Data mapping for consolidation is in fact, not an easy task. To do it correctly requires a deep understanding of the data in each ERP. If you’re using different ERP software, each one probably has its own proprietary data structure, and multiple instances of the same ERP may include custom formats created by local users. Also, the more complex your ERPs are, the more complicated and time consuming the data mapping exercise will be. 

Ultimately, the goal is to minimize the differences across your different ERPs so that consolidating them is easier. Here are a few tips that can help you do that:  

• Identify common data among all the charts of accounts in your different ERPs.
• If possible, develop a standardized chart of accounts template for use across the organization.
• Standardize codes and data types in each ERP to the extent you can to ensure more consistency across data sets.
• Clean up the data in each ERP system before attempting data mapping, correcting errors and omissions in the data. 
• Use a data aggregator or data warehouse capable of managing the consolidated data and monitor the flow of data to track any discrepancies.

Step 2: Data integration

Once you have all the data for each ERP instance mapped out, the next step in the consolidation process is actually getting that data transferred into the receiving system. 

This requires the use of ETL (extract, transform, load) tools that pull the data from the source system, make any changes needed to its structure and format based on your data mapping, and upload it into the target system. Some ERP systems, especially modern cloud-based ones, come with built-in ETL tools or integration platforms to facilitate consolidation. However, there are also third-party tools available. 

Challenges in ERP data consolidation 

Data consolidation across multiple ERPs can be a daunting prospect for businesses as they need to keep up with customer expectations, technology advancements, and compliance needs. Yet with systems designed to work independently, integrating their data can be complex and time-consuming.

The key challenges of consolidating data across multiple ERP systems are:

• Data silos: The incompatibility of systems and lack of integration leads to data silos, which makes gathering all the data needed for any kind of forecasting or analysis a big challenge.  
• Data quality and consistency: Data from multiple ERPs is inconsistent as individual systems have their own  unique structures and format. This leads to duplicate and/or conflicting data, which reduces the accuracy and value of any insights that might be gained from it. 
• Complexity of integration: Integrating multiple ERPs is resource-intensive and usually requires specialized expertise. It is technically challenging as each system supports different data models, APIs, and protocols. Using ETL tools to download and modify data formats is time-consuming and error-prone.
• Real-time data synchronization: Data update frequencies and batch processing times vary for different systems, making real-time data synchronization difficult. Data inconsistencies make mapping and validation challenging.
• Security and compliance: Ensuring data security and regulation compliance becomes more complicated with multiple systems. Each ERP must comply with all relevant regulations to avoid penalties and data breaches.
• Cost and resource allocation: The cost and resource allocation for maintaining integrations among ERPs, managing data quality after consolidation, and ensuring continuous operations are significant.

Tools and best practices for ERP data consolidation

Consolidating data across multiple ERPs is complex as it involves integrating various systems and standardizing data across them. Companies should follow a structured approach to successfully consolidate data from different ERPs.

Here are some useful tools and practices that facilitate effective data consolidation:

• Data integration platforms: Specialized data integration platforms help ensure the seamless flow of data between different ERPs. Such platforms have connectors and APIs that enable consolidation of data across various ERP systems. 
• Extract, transform, load (ETL) processes: ETL tools help to standardize data from various sources before consolidating it. They extract data from diverse ERPs, transform it into a uniform format and then load it into a central repository or data warehouse. ETL processes by design ensure that all data is standardized and ready for comprehensive analysis.
• Middleware solutions: Middleware is an intermediary for routing and formatting data. Middleware solutions bridge data gaps by handling data transfer and mapping between disparate systems, both of which facilitate smooth data transfer across various platforms.
• Master data management (MDM): It is important to maintain a single, consistent, and accurate view across different ERPs. MDM, which is the practice working to ensure the uniformity, accuracy, and semantic consistency of the company’s data. This ensures data consistency for all key business entities and provides reliable data for customers, products, and suppliers.
• ‍Application programming interface (API) management: Businesses often use APIs to enable real-time data exchange between different ERP systems. API management tools help manage data exchange and protect data from unauthorized access and security vulnerabilities.

How financial planning and analysis (FP&A) software can make data consolidation easier

By default, multiple instances of the same ERP creates data silos in your business, not to mention inconsistencies and higher operational costs. While you can use spreadsheets to map and consolidate the data they contain, it is a highly manual and time-consuming process. 

The good news is, there are many types of financial management solutions that can help businesses streamline ERP data consolidation with features that offer:

• Easy data integration: Tools that integrate with popular ERPs, make it easier to pull data from different systems onto a centralized platform.
• Data standardization: FP&A tools can ensure consistency in data mapping for various sources based on predefined rules to generate insights in a standard format.
• Real-time data updates: Tools that offer real-time data updates allow for more accurate and timely financial reporting.
• Data quality control: Software solutions that offer data validation features help to  eliminate discrepancies and data duplication issues to ensure the accuracy of consolidated data.

Drivetrain covers all of these bases and more with financial forecasting capabilities that give you the ability to use consolidated data for strategic forecasting and planning in addition to consolidated reporting. 

Here’s how Drivetrain, a financial consolidation software, supports data consolidation across multiple ERPs:

• Automated data consolidation: You can easily connect each of your ERP systems to flow the data into Drivetrain providing real-time access.  
• User-driven data mapping: After your initial set up, Drivetrain retains the data mapping for each instance of your ERP and facilitates fast and easy in-platform collaboration features to resolve any questions with local data stewards. 
• Currency conversions: Drivetrain simplifies currency conversions, allowing you to seamlessly convert financial data from multiple currencies and account for foreign exchange (FX) gains and losses in your financial reporting. 
• ERP compatibility: With 200+ integrations and the ability to create custom integrations, Drivetrain is compatible with your ERP. 
• Customized models and reports: Drivetrain allows a ton of flexibility in designing custom data consolidation models and generating business-specific reports.

Learn more about how Drivetrain can power your business with accurate data and generate actionable insights for informed decision-making.