Post-Migration Economy In Practice

Objective: Map the entire content estate and extract actionable intelligence before making migration decisions.

• Comprehensive scanning: All source systems (file shares, SharePoint, Box, Documentum, legacy ECM) are scanned non-intrusively to catalog every file, folder, and permission.
• Duplicate detection: Hash-based exact matching identifies identical files (common: 15-25% of content). Fuzzy near-duplicate detection finds "v1 vs v2" scenarios.
• Access pattern analysis: Who accessed what content, when, and how often? This builds usage heat maps that reveal authoritative versions and departmental dependencies.
• Automated classification: Machine learning models trained on industry taxonomies (financial services, healthcare, legal, etc.) classify documents by type, sensitivity, and business function.
• ROT identification: Content not accessed in 3+ years is flagged as Redundant, Obsolete, or Trivial. Typical finding: 40-60% of content falls into this category.
• Risk scanning: Automated detection of PII (Social Security numbers, credit cards, passport data), confidential financial data, legal privilege markers, and trade secrets.

• Executive-ready content intelligence report with ROI projections
• Heat maps showing which departments use which content most heavily
• Risk assessment: permission sprawl, orphaned content, compliance gaps
• Migration scope recommendation: what to move, what to archive, what to destroy

Outcome: Leadership knows exactly what they have, where the value is, and what the risks are—before spending a dollar on migration infrastructure.

 

Objective: Define the governance framework and metadata taxonomy that will govern content in the target environment.

• Business owner assignment: Define rules for assigning content ownership based on access patterns, org hierarchy, and business unit. Validate provisional assignments with stakeholders.
• Retention policy mapping: Map document types to legal/regulatory requirements (Contracts: 7 years, Personnel records: varies by jurisdiction, Marketing collateral: 2 years). Configure Microsoft Purview retention labels.
• Sensitivity classification: Define rules for applying Purview sensitivity labels (Public, Internal, Confidential, Highly Confidential) based on content analysis (PII detection, confidentiality markers, regulatory requirements).
• Metadata taxonomy design: Build metadata schemas by document type and business function. Example: Contracts need Counterparty, Effective Date, Expiration Date, Jurisdiction, Auto-Renewal status. Policies need Effective Date, Owner, Status (Draft, Approved, Superseded).
• Permission rationalization rules: Define least-privilege access model. Legacy "Everyone" groups are broken down. Public folders are re-classified. Sensitive data access is restricted.

• Governance framework document with enforcement workflows
• Metadata taxonomy and SharePoint content type specifications
• Microsoft Purview label configuration (retention + sensitivity)
• AI readiness roadmap: what needs to happen for Copilot to succeed

Outcome: A documented, stakeholder-approved governance model that will be enforced in-flight during migration—not as a post-migration cleanup project.

 

Objective: Execute the migration while simultaneously restructuring, enriching, and governing the data.

This is where the Post-Migration Economy diverges most sharply from traditional migration. Instead of simple file transfer, content passes through an in-flight intelligence layer where it is transformed before entering Microsoft 365.

• Content extraction: Files are opened and parsed—not just copied. Text is extracted from Word, Excel, PowerPoint, PDF (with OCR for scanned documents), legacy formats (WordPerfect, Lotus Notes), and embedded objects.
• Entity recognition: Named entity extraction identifies people, organizations, locations, financial figures, dates, contract terms, and regulatory references within documents.
• Context injection: Industry-specific taxonomies (ISDA for financial services, HL7 for healthcare, ISO standards for manufacturing) and company-specific institutional knowledge (project codes, client abbreviations, department names) are applied as metadata.
• Metadata enrichment: The taxonomy and governance rules from Phase 2 are applied. Every file receives document type, ownership, retention period, sensitivity label, and business context metadata before it enters SharePoint.
• Format optimization: Legacy formats are converted to modern equivalents. Scanned PDFs are OCR'd. Corrupted files are repaired or flagged. Encoding issues are fixed.
• Permission rationalization: Least-privilege access rules are applied. Legacy group memberships are validated. Sensitive data access is restricted. The target environment enforces governance from Day 1.
• Quality validation: Sample Copilot queries are tested against migrated content to validate that AI can reliably read and understand it.

Phase 4: Activation & Value Realization (Ongoing)

Objective: Measure AI adoption, governance compliance, and business value. Optimize continuously based on usage patterns.

What happens post-migration:

• Copilot enablement: Roll out Copilot with confidence, knowing the underlying content is AI-ready. Provide user training that demonstrates reliable AI performance.
• Analytics dashboard deployment: Microsoft Fabric and Power BI dashboards visualize content intelligence—usage patterns, governance compliance, search effectiveness, Copilot performance.
• Governance automation monitoring: Track retention compliance (% of files with assigned labels), sensitivity enforcement (access violations detected and blocked), and lifecycle triggers (files approaching disposition).
• AI performance metrics: Monitor Copilot query success rates, user adoption curves, hallucination incidents, and user satisfaction scores.
• Continuous optimization: Use behavioral data to refine metadata, adjust taxonomies, and improve search relevance over time.

Deliverables:

• Monthly value realization reports showing productivity gains, cost savings, and risk reduction
• Copilot performance benchmarks and optimization recommendations
• Governance compliance scorecards for audit readiness

Outcome: Sustained AI ROI, automated governance, and a competitive advantage built on content intelligence. The investment in in-flight transformation pays dividends every quarter.