- 8 min read
- November 2026
How to Prepare Your Data for Accurate Copilot Results
Deryn Crighton-Smith
When Microsoft Copilot gives an incorrect answer about a customer, account or opportunity, many organisations assume the AI is at fault. In reality, the problem is often the data behind it. Copilot can only work with the information it can access, and poor-quality data inevitably leads to poor-quality answers.
This guide explains how Copilot uses Dynamics 365 and Dataverse data, why data quality matters, and the practical steps you can take to improve Copilot accuracy.
Quick Answer: How to Improve Copilot Accuracy
- Remove duplicate customer records.
- Validate email addresses, phone numbers and postal addresses.
- Standardise how data is entered across the organisation.
- Fill critical fields and relationships.
- Test Copilot against known answers.
- Monitor data quality continuously.
The better your data quality, the more reliable Copilot’s answers will be. However, even with excellent data, important outputs should always be verified before acting on them.
What Is Copilot Data Readiness?
Copilot data readiness is the process of ensuring your CRM and business data is complete, accurate, consistent and trustworthy before using AI-powered tools.
A Copilot-ready CRM typically has:
- No duplicate records
- Accurate contact information
- Standardised data formats
- Clear table relationships
- High field completeness
- Ongoing quality monitoring
Without these foundations, Copilot can retrieve incomplete, outdated or conflicting information and present it confidently, creating unnecessary business risk.
Key Takeaways
- Copilot retrieves and summarises the information you already hold.
- Copilot does not automatically clean or correct poor-quality data.
- Duplicate records, stale contacts, invalid emails and missing fields are among the most common causes of inaccurate responses.
- Clean data improves the reliability of Copilot outputs.
- A small set of real-world test questions can reveal where data quality issues are affecting AI performance.
- The most successful organisations start with one use case, improve the supporting data and then expand.
How Does Copilot Use Your Data to Answer Questions?
Copilot’s answers are grounded in the data it can access. According to Microsoft, Copilot can search Dataverse data, follow relationships between records and generate responses based on the information retrieved.
The process typically works like this:
- A user asks Copilot a question.
- Copilot searches the Dataverse tables available to that user.
- Related records are retrieved through lookups and table relationships.
- Copilot generates a summary, explanation or recommendation based on the retrieved information.
This means data quality directly affects answer quality.
If a customer appears three times in your CRM, Copilot may only pull information from one of the records. If contact details are outdated, Copilot may recommend actions based on obsolete information. The AI is not creating the problem. It is simply reflecting what already exists in the system.
Can Clean Data Make Copilot Completely Accurate?
No. Clean data significantly improves Copilot’s performance, but it does not guarantee perfect answers.
AI-generated content can still contain inaccuracies, misunderstand context or produce incomplete responses. However, high-quality data removes many of the avoidable errors that often undermine trust in AI systems.
Clean data helps prevent:
- Missing customer history
- Duplicate opportunities
- Inaccurate forecasts
- Invalid contact recommendations
- Incomplete summaries
With fewer data quality issues, any remaining AI errors become easier to identify and manage.
Research from Gartner found that 59% of organisations do not formally measure data quality, making it difficult to understand the impact poor data has on business processes and AI initiatives.
Why Does Copilot Give Wrong Answers?
Most inaccurate Copilot responses can be traced back to a small number of data quality issues.
| What You See in Copilot | Likely Cause | How to Check | How to Fix |
|---|---|---|---|
| Customer summary missing recent activity | Duplicate records | Contact and account duplicate rate | Merge duplicates |
| Customer or deal counted twice | Duplicate opportunities or accounts | Matching names and addresses | Deduplicate and prevent re-entry |
| Follow-up suggestions for former contacts | Stale records | Time since validation | Cleanse and suppress obsolete records |
| Scheduled emails bounce | Invalid email addresses | Validation pass rate | Validate emails |
| No results returned | Missing key fields | Field completeness | Fill gaps |
| Different answers to similar questions | Inconsistent data values | Distinct values analysis | Standardise data |
| Incorrect territory or location results | Invalid addresses | Address validation rates | Validate and standardise addresses |
Want to Know Where Your Data Is Affecting Copilot?
Many organisations discover that relatively small data issues have a disproportionate impact on Copilot performance. A free data quality audit can quickly identify duplicate records and validation issues that may be reducing AI accuracy before they become larger business problems.
How Should You Structure Dynamics 365 Data for Copilot?
Data structure matters just as much as data quality.
Copilot relies on tables, columns, relationships and metadata to understand business context. Even accurate data can produce poor results if it is structured inconsistently.
Best Practices
Use Clear Field Names
Good:
- Renewal Date
- Account Owner
- Primary Contact
Poor:
- Date2
- StatusA
- Field4
Standardise Status Values
Avoid multiple ways of expressing the same thing:
- Active
- active
- Active – Ongoing
Instead, use controlled choice values wherever possible.
Maintain Relationships
Missing relationships prevent Copilot from building complete customer context.
Standardise Business Terminology
Choose one format for:
- Countries
- Industries
- Job titles
- Company names
Consistency improves both reporting and AI retrieval.
Define a Single Source of Truth
If customer information exists in multiple systems, establish which system owns each data element. Conflicting records create uncertainty for both users and AI.
How Do You Test Whether Copilot Is Accurate?
Testing is one of the most overlooked parts of AI adoption.
The simplest approach is to compare Copilot responses against answers you already know are correct.
A Practical Testing Framework
- Select 10 to 20 real business questions.
- Determine the correct answers from source records.
- Ask Copilot the same questions.
- Compare results.
- Identify the underlying cause of inaccuracies.
- Correct the data.
- Re-test and measure improvement.
Repeat testing monthly to monitor data quality trends and detect new problems before they spread.
Where Should You Start?
The biggest mistake organisations make is attempting to clean everything at once.
Instead:
Step 1: Choose One High-Value Use Case
Examples:
- Account preparation before meetings
- Pipeline reviews
- Lead follow-up emails
Step 2: Measure Current Data Quality
Establish a baseline before making improvements. This makes ROI measurable.
Step 3: Fix Problems in Order
Recommended priority:
- Validate contact details
- Remove duplicates
- Standardise values
- Fill missing fields
Step 4: Protect Data at Record Creation
Preventing bad data is considerably cheaper than fixing it later.
Step 5: Expand
Apply the same methodology to additional use cases once results have been demonstrated.
Which Data8 Tools Help Improve Copilot Accuracy?
Different data quality issues require different solutions.
| Tool | Purpose | Copilot Benefit |
|---|---|---|
| Data Integrity | Measures and monitors data quality | Identifies accuracy risks |
| Data Validation | Validates and standardises data | Improves location and contact accuracy |
| Duplicare | Finds and merges duplicate records | Prevents fragmented customer histories |
| Data Cleansing | Updates and suppresses obsolete records | Improves customer relevance |
A Monthly Copilot Data Readiness Checklist
AI readiness is not a one-time project.
Use this monthly checklist:
- Review data quality scores
- Run duplicate detection
- Check bounce rates
- Monitor phone validity rates
- Re-test Copilot questions
- Investigate unexpected responses
- Review suppression results
- Validate imports and integrations
Even a lightweight monthly process can significantly improve long-term AI performance.
Ready to See How Accurate Your Data Is Today?
Data8 helps organisations identify the data issues that reduce Copilot effectiveness, prioritise improvements and maintain long-term data quality.
Whether you are planning a Copilot rollout or trying to improve existing results, start by understanding the state of your data.
The organisations seeing the greatest value from Copilot are not necessarily those with the most advanced AI. They are the ones with the cleanest, most trustworthy data.
AI-Ready Data for Copilot FAQs
How do I prepare my data for accurate Copilot results?
Validate contact information, remove duplicate records, standardise data formats, fill missing fields and continuously monitor data quality. Test Copilot against known answers and measure improvements over time.
Does Copilot use Dynamics 365 data?
Yes. Copilot can retrieve and reason over Dataverse data, including related records, subject to user permissions and access controls.
Why is Copilot giving incorrect customer information?
The most common causes are duplicate records, stale contacts, invalid contact details, missing fields and inconsistent data formats. These issues affect the information Copilot retrieves and summarises.
What data problem affects Copilot accuracy the most?
Duplicate records are often the most significant issue because they fragment customer history across multiple records, making it difficult for Copilot to build a complete picture.
How often should I review data quality for Copilot?
Most organisations benefit from monthly reviews that include duplicate checks, validation monitoring, quality scoring and accuracy testing. Continuous prevention at data entry is also recommended.
Will clean data eliminate AI hallucinations?
No. Clean data substantially reduces avoidable errors but cannot completely eliminate inaccuracies in AI-generated outputs. Important decisions should always be verified by a human.