OpenText Lens - Data Visibility Automation

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

1. Repository-to-Repository Data Migration Readiness

Use OpenText Lens - Data Visibility to scan source repositories and identify sensitive, redundant, obsolete, and trivial content before a migration. The same platform can then be used on the target environment to validate that only approved content was moved. This supports cleaner migrations, reduces storage costs, and lowers the risk of carrying compliance issues into the new platform.

• Data flow: OpenText Lens - Data Visibility to OpenText Lens - Data Visibility
• Business value: Faster migration planning, reduced migration scope, lower risk

2. Pre-Merger Content Inventory and Consolidation

During mergers or acquisitions, one instance of OpenText Lens - Data Visibility can inventory content across acquired repositories while the other instance analyzes the receiving organization’s environment. Integration helps compare content profiles, identify duplicates, and determine which repositories should be retained, archived, or retired. This creates a structured approach to post-merger content rationalization.

• Data flow: Bi-directional
• Business value: Faster integration of acquired data, reduced duplication, improved governance

3. Sensitive Data Discovery and Remediation Workflow

One OpenText Lens - Data Visibility deployment can detect sensitive content such as personal data, financial records, or confidential documents in legacy repositories. The findings can be passed to another instance used by governance or remediation teams to validate cleanup actions and confirm that flagged content has been moved, secured, or disposed of appropriately. This supports privacy and compliance programs with measurable remediation tracking.

• Data flow: OpenText Lens - Data Visibility to OpenText Lens - Data Visibility
• Business value: Better compliance control, reduced exposure to sensitive data, auditable remediation

4. Cross-Environment Risk Comparison

Organizations often need to compare data risk across business units, geographies, or storage platforms. Integrating two OpenText Lens - Data Visibility environments allows one team to analyze a source environment while another monitors a different repository set, then consolidate results for enterprise-wide risk reporting. This is useful for prioritizing cleanup efforts based on sensitivity, age, and business relevance.

• Data flow: Bi-directional
• Business value: Consistent risk visibility, better prioritization, stronger governance reporting

5. Archive Validation After Data Cleanup

After content cleanup or retention-based deletion, one OpenText Lens - Data Visibility instance can re-scan the source repository to confirm that obsolete content has been removed. A second instance can analyze the archive or retention store to ensure required records were preserved and remain discoverable. This helps records management teams prove that cleanup actions did not compromise legal or operational retention requirements.

• Data flow: OpenText Lens - Data Visibility to OpenText Lens - Data Visibility
• Business value: Reduced retention risk, stronger audit readiness, verified cleanup outcomes

6. Department-Level Data Ownership and Stewardship Reporting

One OpenText Lens - Data Visibility deployment can be used by IT or central governance teams to scan enterprise repositories, while another can be used by business units to review their own content footprint and ownership responsibilities. Integration enables shared reporting on where data resides, who owns it, and which teams need to act on remediation recommendations. This improves accountability and speeds up decision-making.

• Data flow: Bi-directional
• Business value: Clear ownership, improved collaboration, faster remediation cycles

7. Cloud and On-Premises Content Rationalization

When organizations operate both cloud and on-premises repositories, one OpenText Lens - Data Visibility instance can assess the on-premises environment while the other evaluates cloud content. The combined results help determine which data should remain, move, or be retired before modernization initiatives. This is especially valuable for reducing cloud migration costs and avoiding unnecessary data duplication.

• Data flow: Bi-directional
• Business value: Lower storage and migration costs, better modernization planning, reduced data sprawl