Data challenges in Accounts Receivable for large B2B companies

When we chat about the backbone of any big B2B company, Accounts Receivable (AR) pops right up. It’s super important for keeping the money flowing, kind of like the circulatory system for a business. But as companies get bigger, spreading out across different regions, trying out new business ideas, jumping onto various digital platforms, and even merging with or buying other companies, AR can turn into this tangled mess of data and technical puzzles.

This isn’t just about a few misplaced invoices here or there, we’re talking about real, nitty-gritty data engineering challenges that can throw a wrench into the whole business operation. Let’s dive into what makes AR such a headache for data folks.

What makes accounts receivable a data engineering maze?

Imagine trying to build a perfect Lego castle, but all your Lego pieces are from different sets, some are broken, and you don’t have the instruction manual. That’s pretty much what data engineers face with AR data.

Fragmented systems and data sources

Big companies often use a bunch of different Enterprise Resource Planning (ERP) systems and financial tools. Think of a company that’s grown through acquisitions. They might have one ERP from their early days, another from a company they bought last year, and maybe a specialized finance tool for a specific region. It’s like having several separate brains trying to do the same job.

This usually leads to a few common issues:

• Inconsistent data formats and schemas: One system might record dates as DD-MM-YYYY while another uses MM/DD/YY. Customer names might be "Xenon Corp" in one system and "Xenon Corporation" in another. Some have an amount with signs, others may not be having that but the posting key helps to identify the sign. Some important columns may not be available in one system.
• Payment terms that don’t align: One ERP might use numeric codes like 30 for Net 30, while another uses a text like NET30. Some systems may include discount terms (e.g., “2/10 Net 30”) in a single field, while others store discount % and discount days separately.
• Multiple local currencies without a global standard: You might see invoices in Euros, Pounds, and Dollars, but there’s no easy way to convert them all to a single currency for a unified view. It’s like trying to compare apples and oranges without a common scale
• Inconsistencies in document types: An "invoice" in one system might be a "sales order" in another, making it hard to track documents consistently

For example, a global manufacturing company with operations in Germany, the US, and China might use SAP in Germany, Oracle in the US, and a custom-built legacy system in China. Each system handles customer invoices, payment terms, and currency conversions differently. When the finance team in the US tries to get a consolidated view of global outstanding payments, they have to manually reconcile data from all three systems, leading to delays and errors. This is where data engineers come in, trying to build bridges between these disparate islands of information.

Customer master data inconsistencies

This is where things get personal, so to speak. When you’re dealing with customer information, any inconsistencies can cause a ripple effect.

Here’s what typically goes wrong:

• Duplicate customer records across systems: One customer might appear multiple times, each with slightly different details. It’s like having three different phone numbers for your best friend, and you don't know which one is current
• Conflicting credit terms, contact info, and IDs: A customer’s credit limit might be $100,000 in one system and $50,000 in another. Their contact person might be listed as Sarah in one place and Sara in another
• Differing payment terms and credit scores: Especially after an acquisition, the same customer might have different payment terms or credit scores depending on which system you look at. This makes it tough to know how to deal with them
• No consolidated structure for organizing customers: It’s hard to see all of a customer’s subsidiaries or parent company relationships, making it difficult to understand their overall financial health

These issues can lead to payments being applied incorrectly, errors in calculating balances and collections, more manual work to fix things, and even multiple collection teams chasing the same customer for the same debt. Imagine a telecommunications company say ABC acquiring another one say XYZ, and suddenly they have millions of duplicated customer records. A customer might receive two collection calls for the overdue bill, one from the old XYZ system and one from ABC, leading to frustration and damage to the customer relationship. Data engineers need to build robust Master Data Management (MDM)systems to create a "golden record" for each customer, ensuring a single, accurate view across all systems.

Challenges in obtaining customer 360 data

It's tough to get a complete, comparable Customer 360 view across all parts of a company. This means it’s hard to see how different parts of a customer's business hierarchy are performing. You might want to see how a parent company and all its subsidiaries are doing collectively, but if the data isn't structured consistently, you're stuck evaluating each piece separately.

For instance, a large software company might sell different products to various divisions of a major bank. One division might buy their cloud services, another their on-premise software, and a third their consulting services. Without a unified customer 360 view, the software company might see three separate revenue streams instead of a consolidated view of the bank's total spend and its overall relationship. This makes it hard to identify cross-selling opportunities or assess the bank's overall value. Data engineers need to integrate data from sales, support, marketing, and finance systems to build a comprehensive customer profile.

AR aging, partial payments, and disputes

This is where the nuances of financial operations really kick in. Different parts of a company might have their own ways of doing things, which creates a mess when you try to get a unified picture.

• Custom aging buckets: One division might consider an invoice overdue after 7 days, while another uses the standard 30/60/90-day buckets. This makes it impossible to compare performance consistently
• Varied handling of partial payments and credits: Some systems might automatically apply partial payments to the oldest invoice, while others might require manual allocation, leading to discrepancies
• Operating in multiple currencies: As mentioned before, dealing with multiple currencies without a standardized conversion process means your "overdue" amount in Euros might look different when converted to Dollars, causing confusion

These differences can mess up Key Performance Indicators (KPIs), lead to errors in calculating overdue amounts, delay financial book closures, and make reconciliations a nightmare. Think of a global e-commerce giant. They deal with millions of transactions daily, across dozens of countries, each with its own local payment methods, currencies, and even legal requirements for invoice aging. If a customer in Japan makes a partial payment on an invoice, and the system in the US doesn’t correctly record it, it could lead to the customer being incorrectly flagged as overdue, causing a bad customer experience. Data engineers need to build flexible data models that can accommodate these variations while still providing a consolidated view.

Data quality and governance

This is about trust. If your data isn’t good, you can’t trust your reports or make informed decisions.

Without solid data quality controls, validation checks, and strong data governance across all your data sources, you'll run into problems like:

• Missing or invalid invoice data: An invoice might be missing a purchase order number, or the total amount might not match the line items
• Manual adjustments made outside authorized systems: Sometimes, people might make quick fixes in spreadsheets instead of updating the official system, creating data silos
• Insufficient validation at the point of data entry: If there aren't checks in place when data is first entered, typos and errors can creep in unnoticed

These issues erode confidence in AR data and create operational headaches. Imagine a large healthcare provider. Accurate AR data is critical for revenue recognition and compliance. If a patient's insurance information is entered incorrectly, or a billing code is invalid, it can lead to denied claims and significant revenue loss. Data engineers are responsible for implementing data validation rules at every step of the data pipeline, from data ingestion to data consumption, and establishing clear data ownership and stewardship.

Lack of documentation and data dictionaries

This is a silent killer for data projects. When there’s no clear documentation or data dictionaries, it’s like trying to put together a puzzle without knowing what the final picture is supposed to be, or even what each piece represents.

If thorough documentation or data dictionaries are missing, it becomes much harder to combine different data sources into one unified master dataset. This gap can increase operational risks and slow down both integration work and new innovation projects. It’s like trying to onboard a new employee to a complex system without any training manuals, they’ll struggle to understand how everything fits together.

For example, a multinational logistics company might have several different systems storing data related to shipments, invoices, and customer payments. Without a data dictionary that defines what each field means (e.g., "What does STATUS_CODE '01' mean in this table?"), integrating these systems becomes a monumental task. Data engineers spend countless hours reverse-engineering data schemas instead of building new solutions. Building and maintaining a comprehensive data dictionary and robust documentation is crucial for data team efficiency and collaboration.

Inconsistent reporting and KPIs

This ties everything together. If everyone is measuring things differently, you can’t get a clear picture of how the business is actually doing.

Different entities and departments often use their own ways of calculating things like Days Sales Outstanding (DSO), overdue amounts, and cash forecasts. This leads to confusing and hard-to-explain consolidated reports. It’s like different teams playing different sports but calling them all "football." To fix this, you need to establish standardized metrics.

Think of a large hotel chain. Different hotel properties might calculate their DSO based on local accounting practices, leading to vastly different numbers even if their underlying performance is similar. When the corporate finance team tries to create a consolidated financial report for all properties, these inconsistencies make it incredibly difficult to compare performance accurately and identify trends. Data engineers need to work closely with finance stakeholders to define standardized business rules and build data pipelines that enforce these rules, ensuring consistent reporting across the organization.

What’s next for data engineering in accounts receivable?

The future of data engineering in AR is all about adopting cool new technologies and methods to make things super smooth and efficient. By focusing on a few key areas, companies can really spark innovation and make their financial operations much better.

• Invest in a centralized data warehouse or data lake and gradually build one: Instead of having data scattered everywhere, bring it all together in one place. Think of it like building a super organized library for all your financial information. This doesn't mean you need to do it all at once, you can start small and expand. For instance, a medium-sized manufacturing company could start by centralizing their invoice data from their main ERP system into a cloud data warehouse like Snowflake or Databricks. Once that's stable, they can gradually add data from other systems, like their CRM or payment gateways. This provides a single source of truth for AR data, enabling better reporting and analytics.
• Plan early for data integration and cleansing: Don't wait until you have a huge mess to clean up. Think about how you’ll connect all your data sources and clean up inconsistencies right from the start. This involves defining data mapping rules, implementing data validation checks, and setting up automated data cleansing processes. For example, when a company acquires another, data engineers should be involved from day one to plan how to integrate customer and invoice data from the acquired company’s systems into their existing data infrastructure. They can use tools like Talend or Informatica to extract, transform, and load data, while simultaneously identifying and resolving duplicates and inconsistencies.
• Implement robust master data management (MDM) for unified data: This is how you create that "golden record" for your customers and other key entities. An MDM system ensures that everyone is working with the same, accurate information. A large retail bank could use an MDM solution to create a single, unified view of each customer, combining data from their checking accounts, savings accounts, credit cards, and loan products. This helps in understanding the customer's overall financial relationship with the bank and provides a consistent experience.
• Create and update a data glossary so everyone understands what we mean by 'DSO' or 'AR dues': This is like having a dictionary for your business terms. It makes sure everyone, from finance to sales to IT, speaks the same language when talking about financial data. Imagine a global media company. "Revenue" might mean different things in different regions due to local accounting standards. A data glossary would clearly define what "revenue" means for consolidated reporting, ensuring consistency across all departments.
• Standardize AR KPIs, business rules and reporting logics: Define exactly how you'll measure things like DSO and overdue amounts, and make sure everyone uses the same formulas and rules for reporting. This ensures that when you look at a report, you know exactly what the numbers mean. A global telecommunications company could standardize its DSO calculation across all its subsidiaries, ensuring that comparisons of AR performance are meaningful and accurate. This involves defining the exact calculation methodology, including what counts as "sales" and what constitutes "outstanding."
• Modernize ETL/ELT pipelines with monitoring: Update your Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes, which are responsible for moving and preparing your data. Make sure they’re efficient and have monitoring in place so you can quickly spot and fix any issues. For instance, a fast-growing SaaS company might move from manual data transfers to automated, cloud-native ELT pipelines using tools like Fivetran or Airbyte to move data from their CRM, billing system, and payment processors into their data warehouse. They would also implement monitoring tools like PagerDuty or Datadog to get alerts if a pipeline fails or data quality issues are detected.
• Enforce data quality checks and role-based security: Put checks in place to ensure your data is always accurate and complete. And make sure only the right people have access to sensitive financial information. For example, a financial services firm dealing with highly sensitive customer data would implement strict data quality checks to ensure all details are accurate and complete. They would also implement role-based access control (RBAC) to ensure that only authorized finance personnel can view or modify specific AR data, adhering to regulations in a specific geography.

These enablers allow us to really focus on getting actionable data insights, doing better analytics, understanding customer lifetime value, improving recovery measures, and pushing forward company initiatives. It also means we can engage directly with customer decision-makers when needed, because we have a clear, accurate picture of their account.

The journey to modernize data engineering in Accounts Receivable is a continuous one. It’s about building resilient, scalable, and secure data pipelines that not only handle the current influx of financial data but also anticipate future demands. You don't need a complete overhaul to get moving, just a few targeted changes can make a huge impact. As technology evolves and businesses become even more data-driven, the role of data engineering teams will only become more critical in ensuring the financial health of large B2B companies.