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AI Enhanced Data Mapping

Updated

Use Avid AI to get AI suggestions for field mappings, conditional logic, and data filters when connecting new fundraising data to Avid. You review, accept, or refine each recommendation before it affects your dataset.

Use this article when…

  • You are connecting a CRM integration or uploading a file and need help mapping.
  • You want suggested mappings for common fundraising fields like channel, transaction type, or payment type.
  • You want help creating conditional mappings (including patterns like regular expressions).
  • You want to refine filters using natural language (for example, “exclude gift in kind”).

Applies to

Item Details
Feature AI Enhanced Data Mapping (Avid AI)
Where you’ll use it New file uploads and dataset connections during mapping 
Permissions You need access to configure dataset mappings and filters
What you need A connected integration or uploaded file with columns available for mapping

What it is

Avid AI uses AI to suggest how your source columns (“your data fields”) should map to Avid’s standardized schema (“Avid fields”). It can also propose conditional mappings and data filters to help you standardize reporting and downstream workflows.

Why it matters

  • Speed up onboarding when you connect a CRM or ingest new files.
  • Clean up messy or inconsistently named columns with standardized Avid fields.
  • Reduce rework during CRM migrations (pre-migration analysis, during migration, and post-migration validation).

Core features

Auto-mapping common fields

When you connect a CRM integration or choose a data category, Avid AI can auto-suggest mappings for common fields, such as:

  • channel and sub-channel
  • is recurring
  • is postal
  • transaction type
  • payment type

Mapping suggestions with preview and reasoning

Each suggestion includes:

  • The proposed “your data field” mapped to an “Avid field.”
  • A preview of the resulting output values with counts.
  • An explanation of why Avid AI chose the mapping.

Conditional mapping (custom logic)

For fields that do not map one-to-one, Avid AI can propose conditional rules using:

  • Contains, Equals, and Does Not Equal
  • Is Blank and Is Not Blank
  • Multiple conditions combined with AND / OR
  • Default fallback values for unmatched cases
  • Pattern matching (including regular expressions) when needed
Example: Standardize a channel field

Avid AI can propose rules like:

  • When your data field contains Online → output Digital
  • When your data field contains Mail → output Direct Mail
  • Otherwise → output Other

Use the preview to confirm the outputs match your reporting needs before you click Accept.

Data filter suggestions

Avid AI can suggest inclusion and exclusion filters for datasets. For transactions, it commonly recommends excluding non-gift activity (for example, pledges, reversals, or gifts-in-kind) based on the columns available in your data.

Refine filters using natural language

You can ask Avid AI for specific filter outcomes in plain language (for example, “exclude gift in kind”). Avid AI then looks for matching columns and values to build a proposed filter.

How Avid AI works

Avid AI evaluates multiple signals to recommend the best mapping and filter logic:

  • Source column names, data types, and sample values.
  • Similar mappings from other organizations using the same integration.
  • Related mappings already configured in your dataset.
  • Data quality signals like null counts and distinct values.
Iterative suggestions (what “Refine” does)

Avid AI can iterate on a mapping suggestion in multiple passes. During refinement, it may use questions about your data (like top values, distinct counts, and empty fields) to improve accuracy.

If you see unexpected output values, click Refine and tell Avid AI what to change. For example: “Use X instead of Y” or “Do not output Other.”

Smart safeguards that improve suggestion quality
  • Column validation: Avid AI checks that suggested source columns exist in your dataset.
  • Preview-first workflow: You see sample outputs before you accept changes.
  • Explainability: Avid AI explains the reasoning behind each suggestion.

Where you’ll see Avid AI

New file uploads

  1. Upload your file.
  2. Select the data category (for example, transactions or constituents).
  3. Avid AI generates suggestions for required fields.
  4. Review each suggestion and click Accept or Refine.
  5. Save the mapping with the file.

Dataset connections

  1. Configure a new data source connection.
  2. On the column mapping step, click Suggest Mapping with AI for a field.
  3. Review the suggestion and click Accept or Refine.
  4. For filters, request a filter suggestion and then Accept or Refine.

What you’ll see in the UI

Progress indicator

While Avid AI is generating suggestions, you see:

  • An AI icon (robot/assistant)
  • Message: AI is working to suggest column mappings
  • A progress counter like Completed: X/Y

Field mapping suggestion

When a suggestion is ready, the mapping view shows:

  • Destination Column: the Avid field you are mapping.
  • Proposed Source Column: the recommended “your data field.”
  • Preview Output: a sample of top values with counts.
  • Explain Reasoning: why Avid AI chose the mapping.
  • Actions: Accept or Refine.

Data filter suggestion

When Avid AI suggests a filter, you see:

  • Proposed Filter: the filter conditions.
  • Reasoning: why Avid AI recommended the filter.
  • Actions: Accept or Refine.

Review before you accept

  1. Confirm the destination field matches your intent.
  2. Use the preview to spot unexpected outputs.
  3. If the result looks correct, click Accept.
  4. If it needs changes, click Refine and describe the change.

Always review suggestions before you accept them. Avid AI accelerates mapping work, but you stay accountable for correctness.

Supported data types

  • String (text)
  • Integer (whole numbers)
  • Float (decimal numbers)
  • Date
  • Timestamp
  • Boolean (Yes/No)

Privacy, security, and auditability

Personal and sensitive data detection

Before data is processed for AI suggestions, Avid AI runs sample values through a personal/sensitive data scanner and masks detected values.

  • Column name scanning for terms like email, phone, SSN, or address
  • Sample value pattern detection (for example, emails or phone numbers)
  • Automatic masking/redaction of detected values
  • Warning indicators when sensitive data is detected

Masked values can reduce suggestion quality for sensitive fields. Review these mappings carefully.

Change history

Avid keeps a historical log of mapping changes so you can track who changed what field mapping over time.

Limitations

  • Avid AI suggestions are recommendations and can be wrong.
  • Very unusual data formats may require manual configuration.
  • Some complex transformations may still need human intervention.
  • Masked values may limit AI analysis for some sensitive fields.

Common use cases

  • New client onboarding: accelerate setup when connecting a CRM or importing files.
  • Messy data cleanup: standardize poorly named or inconsistent columns.
  • Agency workflows: speed up mapping for partners who upload files across many clients.
  • CRM migrations: support pre-migration analysis and post-migration validation.

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