The Crucial Role of Data Quality in the Success of Dynamics Copilot in ERP Systems

pjoeckel • July 2, 2024

The Crucial Role of Data Quality in the Success of Dynamics Copilot in ERP Systems

Introduction

In this article, we examine the critical role of data quality in successfully adopting Generative Artificial Intelligence (GenAI) tools such as Copilot and Enterprise Resource Planning (ERP) systems. We’ll explore the evolution of GenAI, its impact on businesses, and how data quality can make or break its implementation.”
A Forrester article titled “Data Quality Is the Primary Factor Limiting B2B GenAI Adoption” discusses the rise of generative artificial intelligence (GenAI) and its profound, transformative implications for businesses. In essence, the authors review the evolution of GenAI, its transformative impact on business, and how data quality influences its use in industry.
The article discusses the evolution of generative artificial intelligence (GenAI) since the release of ChatGPT in 2022, followed by Microsoft's first release of Copilot on February 7, 2023. Initially, these GenAI tools were limited, and trust was low, but now, they are more accessible and integrated into business software platforms.
Large language models (LLMs) are the core technology behind text-based GenAI. While creating LLMs is complex, businesses can now use existing ones like Copilot for Dynamics, lowering the entry barrier for implementing GenAI solutions. However, GenAI's success heavily depends on data quality. Poor data quality can lead to inaccurate GenAI/Copilot outputs, underscoring the crucial need for robust data governance. It's a responsibility that businesses must take seriously to ensure the accuracy and reliability of their GenAI outputs.
Here are the critical data-related insights:
  • GenAI consumes data at a new speed, scale, and complexity level.
  • The success of GenAI heavily depends on data quality.
  • Poor data quality can lead to inaccurate GenAI outputs, emphasizing the need for robust data governance.
  • The primary limiting factor businesses face today is their data quality.
  • GenAI uses data to generate insights unpredictably.
 

Data Quality Conclusion

In conclusion, while GenAI offers promising capabilities and has the potential to revolutionize business operations, the quality of data fed into these systems is paramount. Poor data quality can lead to inaccurate outputs and hinder the successful implementation of GenAI. Therefore, robust data governance is essential. As we move towards a future where AI plays a more significant role in our businesses, we must prioritize maintaining high-quality data to reap the full benefits of these advanced technologies.

The Source of Bad ERP Data

The data quality findings in the article reinforce one of my critical rules for avoiding ERP project cost overruns and delayed go-live dates.
When reviewing the project plans and contracts for a new ERP implementation, beware of the following: any mention of Excel templates for data migration.
Too often, I have seen the responsibility for data migration shift to the customer as a method of saving project costs because the customer would be perfectly capable of using Excel templates to convert data.
This is a surefire recipe for disaster.
The only cost savings associated with this method of ERP data conversion are in the project budget, especially during competitive bidding between implementation partners to win the service contract.
The customer-led data conversion method inevitably leads to time and cost overruns and hefty change orders. The problem is compounded by the fact that the issue is typically found late when data conversion is critical for meeting the project's go-live date.      Moving data between ERP applications via spreadsheets is a surefire method for starting your new ERP solution with data not ready for AI (Artificial Intelligence).
Let's talk about your ERP data migration strategy.

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We are dedicated to ERP project excellence with experienced people, innovative processes, and innovative productivity tools like GYDE365-Discover. Experience - over one hundred years of combined experience selecting and implementing strategic ERP platforms.

 ERP Consultant

Peter Joeckel 

With an IE/OR engineering degree and enterprise software implementation experience starting at Price Waterhouse, Peter Joeckel has been in the business application selection, implementation, and challenged project turn-around business for over thirty years. He credits his industrial engineering degree with his search for better processes and tools to implement complex business application platforms. 
Most recently, he was the lead HandsFree client advisor in the Circle of ERP Excellence lounge and speaker at the Community Summit North America.

HandsFree ERP is dedicated to supporting clients with their ERP initiatives, enabling companies to seamlessly connect users with their ERP partners. By utilizing skilled professionals, streamlined processes, and cutting-edge tools, HandsFree ERP significantly boosts the success rates of ERP projects.

By Peter Joeckel September 11, 2025
Most organizations think data migration is about moving records from A to B. They're wrong. It's about transforming business information into operational truth. Get it wrong, and you're just digitizing your problems at enterprise scale. If you’re a distributor or manufacturer, your business runs on inventory. Simple as that. Everything else, sales, purchasing, operations, revolves around making sure your inventory data is accurate. And yet, so many companies struggle with messy, outdated, or outright incorrect data, setting themselves up for major headaches when it comes time to implement or upgrade an ERP system. For manufacturers and distributors, inventory is the heart of the business. Everything revolves around managing it effectively. In ERP terms, this involves three core processes: 1. Procure-to-Pay – Bringing inventory in from suppliers. 2. Manufacturing or Handling – Transforming or repackaging inventory. 3. Order-to-Cash – Shipping inventory out to customers. At the heart of the problem are three core data sets: customers, suppliers, and inventory . Clean and accurate data here isn’t optional. It’s essential. Let me paint you a picture of what poor data quality really costs: - Financial processes failing because customer master data is inconsistent - Supply chain grinding to a halt because item masters don't match across systems - Month-end closing taking weeks because nobody trusts the numbers - Compliance risks because audit trails are incomplete or incorrect I've seen implementations declare success after migrating millions of records, only to discover they've built a perfect system running on garbage data. The result? Unreliable reporting, broken processes, and users creating shadow systems to track "real" data. Here's what your implementation partner isn't telling you: Data quality issues compound over time. Every day you operate with poor data, you're creating new problems that will need to be fixed later. It's like trying to build a skyscraper on quicksand - no matter how perfect your architecture, IT IS GOING TO SINK. The hard truth: No amount of system optimization can fix bad data. You're either managing data quality now, or you're managing data problems forever. And in D365 F&O, forever gets expensive very quickly. Bills of Materials: The Science That Trips Everyone Up For manufacturers, one of the biggest trouble spots is the Bill of Materials (BOM) . Think of the BOM as a recipe: it defines exactly how components come together to make a finished product, like a “little red wagon.” Each part must be accounted for, structured correctly, and contain only inventory items. Here’s where things go wrong: Many BOMs have too many levels or include non-inventory items like labor and overhead. Legacy systems often force companies to create Frankenstein part numbers that are confusing and error-prone. Process manufacturers with “recipes” face additional complexity because ingredient quality can fluctuate, affecting output consistency. Moving this messy data into a modern ERP without cleaning it first can turn your new system into a nightmare rather than an improvement. Routing: Where Art Meets Science Beyond the BOM, there’s routing , the step-by-step instructions for manufacturing a product. Routing data is critical for understanding capacity, scheduling, and cost management. Capturing work center setup times, labor, material, and overhead costs is key. Most companies simply don’t have this data organized, which means ERP projects often start off on the wrong foot. Planning Ahead: The Key to ERP Success Waiting until the ERP project is live to clean and organize your data is a recipe for disaster. By then, your best engineers and data experts are fully occupied, leaving little time to fix deep-rooted issues. Forward-thinking manufacturers and distributors start data workshops well before the ERP implementation . These workshops: Identify issues in customer, supplier, and inventory data Clean and structure BOMs and routings properly Establish proper part numbering and chart of accounts setups Doing this ahead of time dramatically increases the chances of a smooth, successful ERP deployment—regardless of which system you choose. Bottom line: messy data doesn’t just slow you down, it can completely derail your ERP implementation. Start early, clean it up, and structure it correctly. Your future self (and your new ERP system) will thank you. 
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