Data Quality for AI: What to Review in Your ERP and CRM

Data quality for AI determines whether an assistant can report how much inventory you have available or ends up mixing physical stock with committed inventory. Before connecting your ERP and CRM, you need to review what each system contains, how it identifies customers and products, and what each figure means for the business.

On October 27, 2026, Oracle announced AI Data Platform capabilities for sharing business context across applications and agents. Its AI Discovery agent proposes concepts and relationships based on schemas, semantic models, and documentation; business specialists must review and approve them. The takeaway is not that you should adopt Oracle, but that shared definitions require human validation.

If sales, warehouse, and administrative teams have different understandings of “active customer” or “sale,” connecting their systems will not eliminate those differences. This guide covers how to prepare operational data—not queries about internal documents—so an integration can work with consistent information.

1. What Data to Prepare Before Connecting AI

Start with a specific question: What decision or task should AI support? To check availability and sales follow-up, you do not need to copy every database; you need to identify the records that support those answers.

Prepare this initial inventory:

• Customers: internal identifier, legal business name, Mexican tax ID (RFC) when applicable, branches, contacts, and sales status.
• Products: SKU, description, unit of measure, packaging options, unit conversions, and active or discontinued status.
• Inventory: product, warehouse, stock on hand, reservations, holds, and pending inventory movements.
• Sales transactions: opportunities, orders, invoices, cancellations, and returns, along with their relationships and statuses.
• Context for each record: source system, event date, last modification, and synchronization date.

For each dataset, document how it is obtained: API, controlled query, export, or manual entry. Also identify required fields, missing values, and access permissions. Preparing data for artificial intelligence means selecting relevant information, not granting unrestricted access.

In an enterprise AI integration, this map helps define what to query and what to validate before responding.

2. Hypothetical Example: Three Systems, Three Different Figures

Imagine a Mexican materials distributor. The following data is illustrative, not results from an actual client. Its team wants an agent to answer how much of a product it can sell and how sales are performing.

Source	Inventory Shown	Sales for the Period
ERP	80 units available	9 invoices issued
CRM	100 units in a sales field	12 won opportunities
Spreadsheet	110 units from an earlier count	10 orders recorded

Upon review, the team discovers that the ERP records 100 physical units and 20 reserved units, leaving 80 available. The CRM still shows physical stock from a previous synchronization. The spreadsheet reflects a snapshot taken before the latest inventory movements.

The sales figures do not measure the same thing either: a won opportunity does not necessarily have an order and an invoice. Figures can be correct within their own context and still produce an incorrect answer when mixed together.

The solution is not to ask the model to choose the “most reliable” figure. It is to establish that availability comes from the ERP, explain how it is calculated, and show the as-of time. For sales, you first need to clarify whether the query concerns opportunities, orders, or invoiced sales.

3. Data Quality for AI: Identifiers and Duplicates

Without consistent identifiers, an integration may treat the same customer as multiple companies or add together different packaging sizes of a product.

Standardize Without Losing the Original Records

Create a mapping table between the identifiers in each system and a master identifier. For example, ERP customer C-204 and CRM customer CTA-89 may point to the same account, while retaining both codes to trace their origins.

Review the following:

• Every customer, product, and warehouse has a stable identifier.
• Relationships between orders, customers, and products point to existing records.
• Units are standardized: individual items, boxes, and packs are not added together without conversion.
• Historical codes and discontinued products remain traceable.

Review Duplicates Before Cleaning Them Up

Normalize spacing, phone number formats, and text formatting before looking for matches. The RFC can help, but it should not be the only rule: a legal business entity may have several legitimate branches or operational accounts.

Classify confirmed matches and potential duplicates. Merge only under approved rules, preserve history, and leave ambiguous cases for review. Deleting records based on similar names can break relationships with orders and invoices.

4. Agree on What Each Business Concept Means

Create a brief dictionary that sales, operations, and administrative teams can approve. Each concept should include a definition, a formula where applicable, exclusions, an authoritative source, and an owner.

These are proposals for discussion, not universal definitions:

Concept	Definition to Agree On	Decision Needed
Active customer	Customer with a valid transaction within the defined period	Which transaction counts and which period applies
Available inventory	Usable stock minus reservations and holds, without deducting them twice	How to handle in-transit inventory, returns, and damaged goods
Net invoiced sales	Invoiced sales minus agreed adjustments	Taxes, currency, cancellations, and credit memos
Open order	Order with quantities still awaiting fulfillment	How to handle partial deliveries and cancellations

Designate the authoritative source for each data element, not necessarily for an entire system. The CRM may govern opportunities; the ERP, invoicing; and the warehouse team, counts subject to reconciliation.

Also align time zones, cutoff dates, and currencies. Two reports are not comparable if one uses the order date and the other uses the invoice date. Track dictionary versions so you can explain subsequent changes in results.

5. Assign Owners and Update Frequencies

An initial cleanup loses value if no one maintains the rules. For each dataset, assign a business owner to approve its meaning and a technical owner to oversee its transfer.

The update frequency should reflect the use case and the systems’ capabilities. This framework is a guide:

Data	Business Owner	Update Criteria
Stock and reservations	Operations or warehouse team	Event-driven when feasible; define a maximum delay for committing to availability
Customers and contacts	Sales	When additions or changes are approved, with periodic duplicate reviews
Invoices and adjustments	Administration	After they are recorded; reconcile at operational close
Catalog and unit conversions	Catalog owner	With every authorized change, before using new packaging options

Distinguish the data’s last modification from the last successful synchronization. A connection can run successfully and retrieve information that the responsible team has not yet updated.

Establish what happens if the acceptable delay is exceeded: display a warning, block an action, or request confirmation. AI process automation must account for these exceptions, not just the ideal workflow.

6. Validate the Integration Before Allowing Actions

Start with read-only queries and expected answers approved by the business. Include normal situations and exceptions: partial deliveries, duplicate customers, returns, reserved inventory, and synchronization failures.

Use this acceptance checklist:

• Completeness: critical fields are present; a blank is not interpreted as zero.
• Uniqueness: no unresolved duplicates affect the use case’s results.
• Validity: dates, statuses, currencies, and units follow the agreed rules.
• Integrity: relationships between records are valid and leave no orphaned transactions.
• Consistency: totals reconcile using the same definition and cutoff.
• Timeliness: information meets the maximum acceptable delay.
• Traceability: each answer makes it possible to identify the source and query time.

Define acceptance thresholds based on the impact of an error. If a unit conversion is missing or sources contradict one another, the agent should flag the issue and request a review, not improvise a figure. Authorizing actions requires additional controls beyond those needed for a query.

7. Conclusion: Assess Your Sources Before Connecting

Data quality for AI is not about making every dataset across the company flawless before you begin. It is about preparing the data needed for a use case: compatible identifiers, controlled duplicates, approved definitions, clear owners, and verifiable updates.

Before integrating AI with your ERP and CRM, put together a source map, a concept dictionary, and a prioritized list of discrepancies. These will help you distinguish what to correct at the source, what to transform during integration, and which decisions require business approval.

At Slashweb, we can help you evaluate those sources and their connections. Contact us to discuss a data and integration assessment and define a focused starting point for your operations.