Introduction

Enterprises use various applications across departments to solve for their pressing business needs and data has traditionally been a secondary thought. Though the recent applications have focused on integrations but the data residing within two applications do not talk to each other. This has caused Data to be everywhere but with GenAI in focus, its importance within the enterprise has grown tremendously. Organizations rely heavily on their Enterprise Resource Planning (ERP) systems such as SAP and Oracle to streamline operations and make informed decisions. However, even the most robust ERPs can’t protect enterprises from the damaging impact of poor data quality. Especially in critical functions like Finance & Accounting, Procurement, and Sales Operations, seemingly small errors can ripple through the entire organization, causing delays, compliance issues, and financial risks.

At Midoffice Data, we’ve seen firsthand how these challenges play out, and we understand the stakes involved for enterprises managing billions in transactions and inventories. Let’s explore how data quality issues arise, the implications they carry, and the steps you can take to address them.

Understanding Data Quality in Systems

ERPs are designed to bring various departments together on a single platform, but the data feeding these systems often comes from disparate sources or manual entry, creating plenty of room for error. Picture this: an organization with thousands of vendor accounts across several countries, each with slightly different systems, processes, and naming conventions.

When this data flows into the ERP without rigorous quality checks, it can lead to:

• Duplicate customer or vendor records
• Tax number mismatches
• Un-mapped  cost center / Profit Center
• Duplicate Purchase orders

These may seem like mundane issues, but they lead to operational inefficiencies, financial misstatements, and strained relationships with customers and vendors.

Common Data Quality Challenges: Real-World Examples

Let’s delve into some of the most common data quality issues that we encounter in Finance, Procurement, and Sales Operations.

1. Accounting Documents with Invalid Vendor & Customer Accounts

Imagine submitting an invoice to a customer, only to realize that the customer account doesn’t match the master data OR the payment terms are not up to date compared to Sales Order. Inaccurate vendor or customer master data details  can result in manual work for teams to process invoices that never gets paid. This often happens when master data isn’t synchronized with transaction data or updated regularly.

2. Tax Number Mismatches

Mismatches in tax numbers between your ERP system and external records can lead to incorrect tax filings and regulatory fines. The cost of manually reconciling these errors—especially during audits—can be enormous.

3. Cost Centers or Profit Center not in the Organization Hierarchy

It’s a story we hear all too often. A finance team processes an expense, only to find out that the cost center doesn’t exist in the official organizational hierarchy. This error not only affects internal P&L reporting but also compliance with financial regulations, leading to delays in closing the books and inaccurate reporting.

4. Non-English Characters in English Description Fields

In global organizations, the mixing of language characters is a common issue. Non-English characters in description fields can cause issues during integration with other systems, break automated workflows, or even prevent analytics engines from reading and interpreting the data correctly.

5. Duplicate Vendor or Customer Names

Duplicate records create confusion, result in incorrect invoicing or double payments, and increase reconciliation efforts. For instance, if a single vendor has multiple records with slight name variations, allocating the costs to the right vendor account delays the books closing process or errors in reporting for both procurement and financial processes.

6. Purchase Orders with Over-Invoiced Quantities

We always see exceptions and disputes between vendors and enterprises for mismatched quantities ordered and received causing delays in reporting financial forecasts and inventory tracking. These discrepancies slow down operations and often result in back-and-forth negotiations between departments, which impacts cash flow.

The Financial Impact of Poor Data Quality

Poor data quality isn’t just an IT issue—it’s a bottom-line issue. Here’s why:

• Delayed Decision-Making: Decision-makers, especially in finance, rely on accurate data to create budgets, forecasts, and business strategies. With poor data quality, they either receive incorrect data or are forced to spend extra time and resources reconciling errors.
• Compliance Risks: Tax mismatches or errors in financial reporting can lead to regulatory penalties, delayed audits, and reputational damage.
• Customer and Vendor Relationships: Miscommunication caused by inaccurate customer or vendor data can lead to mistrust, delayed payments, and damaged relationships.
• Operational Inefficiencies: Duplicate records, missing tax information, and inaccurate purchase orders slow down operations. This trickles down into procurement delays, incorrect inventory levels, and potential loss of revenue.
• AI & Automation issues: Data quality issues can impact the results from the AI solutions such as sales forecasting or Accounting chatbot being implemented or can break the automation workflow and require teams to restart the process.

The Path to Better Data Quality

While the consequences of poor data quality are daunting, the solution lies in applying stringent data quality checks and leveraging the right tools. Here are some strategies for Finance teams to consider:

1. Implement Data Governance Frameworks: Build clear policies on how data is entered, managed, and maintained across the organization. Establish data stewards responsible for overseeing the accuracy of critical datasets.
2. Automated Data Validation: Automated checks should be integrated into ERP workflows to catch common errors like duplicate records or invalid tax numbers before they cause downstream issues. If this seems to be a huge effort then another alternative would be to build domain-specific data quality rules within the warehouse that reports out the financial metrics. Domain data-quality rules can be checks such as – Mismatches in the customer/vendor names within the documents (Invoices, PO, Master data etc), Bad date formats such as PO date greater than Invoice date, inconsistent date formats that can impact aging and payable/receivables calculations, Duplicate names
3. Regular Master Data Audits: Conduct periodic audits of your master data to ensure alignment with real-world entities. Cross-check vendor, customer, and tax records to spot discrepancies early.
4. Leverage Data Reconciliation: Invest in automation reports that continuously reconcile transactional data against master data to detect mismatches in real time. This proactive approach minimizes the impact of data quality issues on your operations. For example, in our platform we do automated reconciliation checks between the various input sources to provide visibility on data quality issues across the different sources.
5. Push Data Quality issues Upstream: We do not recommend fixing data quality issues in a data warehouse or data lake but implementing processes in a warehouse that can report out these data quality issues and also identify the sources of these data quality issues.

The above solutions may appear very simple but are the most difficult to implement. Having worked with customers to solve these issues, we’ve learnt that Finance and Operations teams are the most resilient teams and are very quick to identify data quality issues but it impacts delivering business outcomes. Hence, solving these problems ensure a smooth financial and operational reporting.

Closing Thought: The true value of Application data lies in its accuracy and reliability. Without data quality, even the most sophisticated ERP systems can become roadblocks rather than enablers of success.

Midoffice Data: Your Partner in Data Excellence

At Midoffice Data, we work with Finance and Operations teams in delivering business outcomes and solving the complex data quality issues that enterprise teams face is one of the challenges that we help. We understand that no two businesses are the same, which is why we tailor our solutions to address the unique challenges within your ERP systems. From automated data validation to continuous reconciliation, we empower Finance, Procurement, and Sales Operations teams to unlock the true potential of their data.

The cost of poor data quality is too high to ignore, but with the right strategies and technology, it’s a challenge that can be overcome. Reach out to our team at Midoffice Data to learn how we can help you improve data quality, drive efficiency, and achieve better business outcomes.