Azure Computer Vision - OpenText Lens - Data Visibility Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and OpenText Lens - Data Visibility

Azure Computer Vision and OpenText Lens - Data Visibility complement each other well in enterprise content and information governance programs. Azure Computer Vision extracts meaning from images, scans, and visual files, while OpenText Lens identifies where unstructured content resides, how much of it exists, and whether it is sensitive, redundant, or obsolete. Together, they help organizations classify visual content at scale, reduce risk, improve searchability, and support cleanup or migration initiatives.

1. Automated classification of image and scan repositories

Use Azure Computer Vision to analyze images, scanned documents, and PDFs stored across file shares, content repositories, and archives. Pass extracted text, detected objects, and image tags into OpenText Lens to enrich inventory records and classify content by business context.

• Data flow: Azure Computer Vision to OpenText Lens
• Business value: Faster identification of what visual content exists and where it resides
• Operational outcome: Reduced manual review effort for large unstructured content inventories

2. Sensitive content discovery in scanned documents and images

Organizations often store contracts, IDs, invoices, and forms as images or scans. Azure Computer Vision OCR can extract text from these files, and OpenText Lens can then evaluate the extracted content to identify sensitive or regulated information such as personal data, account numbers, or confidential business terms.

• Data flow: Azure Computer Vision to OpenText Lens
• Business value: Better visibility into hidden risk in image-based content
• Operational outcome: Improved compliance reporting and risk remediation prioritization

3. Pre-migration content profiling for legacy repositories

Before migrating file shares, archives, or document management systems, Azure Computer Vision can process visual assets to extract metadata, text, and image attributes. OpenText Lens can then use that enriched metadata to determine which content is redundant, obsolete, or still business relevant, helping teams decide what to migrate, archive, or delete.

• Data flow: Azure Computer Vision to OpenText Lens
• Business value: Lower migration cost and reduced target-system clutter
• Operational outcome: Cleaner migration scope and better data quality

4. Brand and logo detection for governance and content cleanup

Marketing, legal, and compliance teams can use Azure Computer Vision to detect logos, branded assets, and product imagery in shared repositories or social media archives. OpenText Lens can then map where those assets are stored, identify duplicates or outdated versions, and support cleanup of obsolete branded content.

• Data flow: Azure Computer Vision to OpenText Lens
• Business value: Better control over brand assets and reduced exposure to outdated materials
• Operational outcome: Easier removal of obsolete or noncompliant visual content

5. Discovery of customer-submitted images for case management and quality review

In industries such as insurance, retail, and manufacturing, customers submit photos for claims, returns, or quality issues. Azure Computer Vision can identify objects, defects, or text in those images, while OpenText Lens can help locate related content across repositories and determine whether similar cases, duplicate submissions, or sensitive attachments already exist.

• Data flow: Azure Computer Vision to OpenText Lens
• Business value: Faster investigation and better case consolidation
• Operational outcome: Reduced duplicate handling and improved operational consistency

6. Accessibility and records enrichment for archived visual content

Azure Computer Vision can generate alt-text and OCR output for images and scanned files, making content more accessible and searchable. OpenText Lens can store and analyze that enriched metadata to improve discoverability, support records classification, and help teams apply retention or governance policies more accurately.

• Data flow: Azure Computer Vision to OpenText Lens
• Business value: Better accessibility and stronger information governance
• Operational outcome: More searchable archives and improved policy enforcement

7. Bi-directional governance workflow for high-risk visual content

OpenText Lens can identify repositories or folders with high concentrations of sensitive, obsolete, or duplicate content. Those locations can then be sent to Azure Computer Vision for deeper analysis of images and scans, such as OCR, object detection, or content tagging. The enriched results are returned to OpenText Lens to support remediation workflows and governance decisions.

• Data flow: OpenText Lens to Azure Computer Vision and back to OpenText Lens
• Business value: More targeted analysis of high-risk content areas
• Operational outcome: Efficient remediation with fewer unnecessary scans

Together, these integrations help organizations move from simple content inventory to actionable visual content governance, improving compliance, reducing storage waste, and making unstructured data easier to manage across business and IT teams.