Prodigy - OpenText Lens - Data Visibility Integration and Automation

Common Integration Use Cases Between Prodigy and OpenText Lens - Data Visibility

1. Curate high-value training data from sensitive unstructured content

Data flow: OpenText Lens - Data Visibility ? Prodigy

Use OpenText Lens - Data Visibility to scan file shares, content repositories, and collaboration platforms to identify unstructured documents, images, emails, and text that are relevant for AI model training. After filtering out sensitive, redundant, or obsolete content, send approved datasets into Prodigy for annotation.

• Reduces manual data hunting by data scientists and analysts
• Prevents accidental use of restricted or low-quality content in model training
• Speeds up dataset preparation for NLP, document classification, and computer vision projects

2. Build compliant annotation workflows for regulated industries

Data flow: OpenText Lens - Data Visibility ? Prodigy

Organizations in healthcare, financial services, insurance, and legal sectors can use OpenText Lens - Data Visibility to detect personally identifiable information, confidential records, and regulated content before it is sent to Prodigy. Only approved, masked, or de-identified content is routed for labeling.

• Supports privacy and governance requirements during AI development
• Helps legal and compliance teams approve training data before annotation begins
• Reduces risk of exposing sensitive content to annotators or external labeling teams

3. Prioritize annotation based on content inventory and business relevance

Data flow: OpenText Lens - Data Visibility ? Prodigy

OpenText Lens - Data Visibility can classify and inventory large unstructured repositories, then pass metadata such as document type, source system, sensitivity level, and business domain to Prodigy. Data science teams can use that context to prioritize which content should be labeled first for the highest model impact.

• Improves active learning by focusing on the most valuable content sets
• Helps teams balance model performance needs with business priorities
• Enables faster labeling of high-impact categories such as contracts, claims, support cases, or product images

4. Create labeled datasets for information classification and records management models

Data flow: OpenText Lens - Data Visibility ? Prodigy

Enterprises can use OpenText Lens - Data Visibility to identify representative samples of documents across repositories and then send them to Prodigy for manual labeling. Those labels can train custom models for document classification, retention categorization, or records identification.

• Supports automation of content classification at scale
• Improves accuracy of downstream governance and retention workflows
• Reduces dependence on manual review for large content volumes

5. Improve migration planning with labeled content samples

Data flow: OpenText Lens - Data Visibility ? Prodigy

During content migration or repository consolidation programs, OpenText Lens - Data Visibility can identify content types, ownership, age, and sensitivity across source systems. Selected samples can then be labeled in Prodigy to train models that distinguish content to migrate, archive, redact, or dispose.

• Helps migration teams segment content more accurately before cutover
• Reduces migration cost by avoiding unnecessary movement of obsolete data
• Supports defensible disposition and archive decisions

6. Feed annotation outcomes back into data governance rules

Data flow: Prodigy ? OpenText Lens - Data Visibility

Labels created in Prodigy can be exported back to OpenText Lens - Data Visibility as enriched metadata or classification signals. This allows governance teams to refine content discovery rules, sensitivity tagging, and remediation workflows based on human-reviewed examples.

• Improves the accuracy of automated content classification
• Creates a feedback loop between AI model development and governance operations
• Helps standardize how similar content is handled across repositories

7. Validate AI-assisted content discovery with human-labeled ground truth

Data flow: OpenText Lens - Data Visibility ? Prodigy

OpenText Lens - Data Visibility can surface candidate content sets based on AI-driven analysis, and Prodigy can be used to label a subset of those results to create ground truth. The labeled samples can then be used to validate or tune classification models and improve confidence in repository-wide discovery outcomes.

• Strengthens model governance and auditability
• Helps data owners verify that sensitive or business-critical content is being detected correctly
• Supports continuous improvement of discovery and classification accuracy

8. Accelerate cross-functional AI and governance collaboration

Data flow: Bi-directional

Data science, compliance, records management, and business teams can collaborate by using OpenText Lens - Data Visibility to identify candidate content and Prodigy to label it for model development. The resulting labels and metadata can then be shared back to governance teams for policy enforcement and to AI teams for retraining.

• Creates a shared workflow between AI development and information governance
• Reduces rework caused by poor dataset selection or incomplete classification
• Improves alignment between model outcomes and enterprise data policies