Quality Best Practices to Support AI Agents
AI agents are only as good as the data they work with. Poor data quality doesn’t just reduce agent effectiveness—it actively undermines it, leading to incorrect responses, poor recommendations, and eroded user trust.
At Purus Consultants, we’ve seen how data quality can make or break AI implementations. Here’s what you need to know about preparing your data to support AI agents effectively.
Why Data Quality Matters for AI
Traditional software follows explicit rules: if the data is poor, the software might produce incorrect results, but the logic remains predictable. AI agents are different—they reason about data, drawing inferences and making decisions based on patterns they detect.
Poor data quality creates several problems:
- Inaccurate Responses – Agents answering queries based on incorrect data damage customer relationships and organizational credibility.
- Biased Recommendations – Incomplete or skewed data leads to biased agent behaviour, potentially violating fairness principles or regulations.
- Low Confidence – Users quickly lose trust in AI that provides inconsistent or obviously wrong information.
- Wasted Effort – Time spent debugging agent behaviour often traces back to data quality issues that should have been addressed upfront.
- Compliance Risk – GDPR’s data accuracy requirements mean poor data quality can create legal liability.
- Investing in data quality before deploying AI agents saves enormous pain later.
The Pillars of Data Quality
Comprehensive data quality rests on several foundations:
1. Accuracy
Data must correctly represent reality. This seems obvious, but inaccuracies creep in through:
– Manual data entry errors
– Outdated information that hasn’t been refreshed
– System integration issues that corrupt data in transit
– Migration problems when moving between systems
Best Practices:
– Implement validation rules at point of entry to prevent obvious errors
– Regular audits comparing data to source systems or external references
– Automated data quality checks flagging suspicious values
– Clear ownership for data maintenance responsibilities
– Processes for investigating and correcting inaccuracies
2. Completeness
Missing data prevents AI agents from having full context. Common gaps include:
– Required fields left blank
– Partial records where only some information was captured
– Integration failures where data should have synced but didn’t
– Historical data never backfilled when systems were implemented
Best Practices:
– Make critical fields required where appropriate
– Regular completeness reports identifying records with missing information
– Data enrichment processes filling gaps from external sources
– Clear escalation when incomplete data blocks important processes
– Consider whether “unknown” is different from “blank” for your use case
3. Consistency
The same information should be represented the same way throughout your system. Inconsistency manifests as:
– Different spellings or formats for the same value (“UK” vs “United Kingdom” vs “Great Britain”)
– Duplicate records with slightly different information
– Conflicting data across integrated systems
– Unstandardised picklist values
Best Practices:
– Use picklists rather than free text wherever possible
– Implement standardisation rules (e.g., converting addresses to standard formats)
– Duplicate detection and merging processes
– Master data management for key entities
– Data governance defining canonical formats
4. Timeliness
Data must be current enough for its purpose. Stale data creates problems:
– Agents providing outdated information
– Business decisions based on old insights
– Customer frustration when AI doesn’t reflect recent interactions
Best Practices:
– Real-time or near-real-time integration where timeliness matters
– Clear data freshness requirements for different data types
– Monitoring alerting when data hasn’t updated as expected
– Retention policies archiving or deleting data that’s no longer relevant
– Regular data refresh processes for slower-changing reference data
5. Relevance
Not all data is useful for AI. Irrelevant data clutters the picture and can mislead agents. This includes:
– Obsolete fields no longer used
– Test data mixed with production data
– Information too granular for its actual use
– Data collected “just in case” without clear purpose
Best Practices:
– Regular audits identifying unused fields and obsolete data
– Clear data retention policies removing irrelevant historical data
– Separation of production and test environments
– Purpose-driven data collection (collect only what you’ll actually use)
Preparing Data for AI Agents
Beyond general data quality, AI agents have specific requirements:
Structured Data
AI agents excel at reasoning about structured data with clear schemas. Ensure:
– Critical data is in properly defined fields, not buried in free text
– Relationships between objects are explicitly modelled
– Consistent data types (dates as dates, numbers as numbers, not strings)
– Meaningful field labels and help text (AI uses this metadata)
Unstructured Data
AI can also work with unstructured content (documents, emails, support tickets), but preparation helps:
– Consistent formatting (e.g., standard email templates)
– Proper categorisation and tagging
– Readable text extraction from PDFs and documents
– Removal of irrelevant content (signatures, disclaimers, boilerplate)
Training Data
If you’re customising AI models, training data quality is critical:
– Representative of real-world data the AI will encounter
– Balanced across different scenarios (not skewed toward one type)
– Labelled accurately for supervised learning
– Sufficient volume for the model to learn effectively
Implementing Data Quality in Salesforce
Salesforce provides numerous tools for maintaining data quality:
Validation Rules
Prevent bad data at point of entry:
– Required field checks
– Format validation (email addresses, phone numbers)
– Cross-field validation (end date must be after start date)
– Allowed value ranges
Duplicate Management
Identify and merge duplicate records:
– Matching rules detecting potential duplicates
– Duplicate rules alerting or preventing creation
– Merge processes consolidating duplicates into single records
Workflow and Flow Automation
Automate data quality maintenance:
– Standardisation rules (capitalisation, format consistency)
– Data enrichment from external sources
– Alerts when data quality issues are detected
– Scheduled processes for data cleanup
Data Cloud Capabilities
Data Cloud includes data quality features:
– Identity resolution across multiple sources
– Data harmonisation into unified schemas
– Data quality scores and monitoring
– Reference data management
Data Import Tools
When importing data:
– Data Loader with validation and transformation
– Jitterbit, Talend, MuleSoft for complex integrations
– Einstein Data Detect for identifying quality issues
Establishing Data Governance
Technical tools alone don’t ensure data quality—you need processes and accountability:
Data Stewardship
Assign clear ownership:
– Who owns each data domain (accounts, contacts, opportunities)?
– Who approves data standards and definitions?
– Who investigates and resolves data quality issues?
Data Standards
Document how data should be structured:
– Naming conventions
– Allowed values for key fields
– Format standards (dates, addresses, phone numbers)
– When to create new records vs. update existing ones
Data Quality Metrics
Define and track KPIs:
– Percentage of records with complete critical fields
– Duplicate rate
– Data age (how long since last update)
– User-reported data issues
– AI agent confidence levels (if available)
Regular Audits
Schedule periodic reviews:
– Automated reports identifying quality issues
– Sample-based manual reviews
– User feedback on data quality problems
– Comparison with external data sources
Continuous Improvement
Act on findings:
– Root cause analysis for recurring issues
– Process improvements preventing problems
– System enhancements automating quality maintenance
– Training addressing user behaviour issues
Data Quality for Specific Use Cases
Different AI agent use cases have different data quality priorities:
Customer Service Agents
Critical data:
– Complete customer contact information
– Accurate product and service records
– Up-to-date case and interaction history
– Current product documentation and FAQs
Sales Agents
Critical data:
– Comprehensive lead and contact information
– Accurate opportunity and pipeline data
– Complete activity history
– Current pricing and product information
Marketing Agents
Critical data:
– Segmentation criteria fields
– Engagement history across channels
– Preference and consent information
– Campaign performance metrics
Prioritise data quality in areas directly supporting your agent use cases.
Common Data Quality Pitfalls
Avoid these mistakes:
- One-Time Cleanup – Data quality isn’t a project; it’s an ongoing discipline. Processes must sustain quality continuously.
- Over-Reliance on Technology – Tools help, but cultural change and process discipline matter more. Users must care about data quality.
- Perfectionism – Don’t wait for perfect data before deploying AI. Establish “good enough” thresholds and improve iteratively.
- Ignoring User Feedback – Frontline users spot data quality issues first. Listen to them.
- Lack of Executive Support – Data quality initiatives need sponsorship and resources. Without executive backing, they stall.
The Purus Approach
Our data quality methodology includes:
1. Current State Assessment – Comprehensive audit identifying quality issues and root causes
2. Prioritisation – Focus on data most critical for AI and business processes
3. Quick Wins – Address obvious issues providing immediate value
4. Process Design – Establish sustainable data quality processes
5. Tool Configuration – Implement Salesforce features enforcing quality
6. Training and Adoption – Build quality-conscious culture
7. Monitoring and Iteration – Track metrics and continuously improve
Our goal is data quality that enables AI whilst remaining practically achievable.
Ready to Improve Your Data Quality?
If you’re planning to deploy AI agents or struggling with data quality in your current Salesforce environment, we can help.
Get in touch to discuss your data quality challenges and how we can support you in building the foundation for effective AI.