Data Harmonization

When systems can't integrate,
the problem is classification.

Hypericum installs a semantic control plane across your enterprise data: governed domain intelligence that replaces fragmented taxonomies with versioned, auditable structure your systems can actually use.

The challenge

Most data fragmentation problems are not crises. They are costs that become normal.

Your analytics team reconciles before every report

Cross-system or cross-entity numbers do not match until someone manually harmonizes them. The reconciliation happens every time, by the same people, before the numbers are trusted enough to act on. It is treated as workflow rather than problem.

Your AI projects stall between proof of concept and production

The demos work. Then the real data arrives, and the model produces inconsistent, unreliable, or confidently wrong outputs. The root cause is classification: the same entity described differently across systems, the same cost code used inconsistently across divisions. The AI is not broken. The foundation beneath it is.

No one can see the full picture on a customer, supplier, or product

Different systems hold different parts of the record. A customer appears three ways across CRM, finance, and operations. A supplier has four vendor codes. A product category means one thing in procurement and another in reporting. The consolidated view everyone needs exists only as a manual exercise.

New systems inherit old problems

A CRM migration, a new BI tool, an ERP upgrade: each one starts with a data extraction that reveals the same underlying fragmentation. The new platform cannot fix what the classification layer did not govern. The problem travels.

These are not symptoms of a bad system. They are symptoms of a classification layer that was never built.

What we do

Hypericum's approach treats classification as infrastructure, not a clean-up exercise. We build the semantic layer your systems depend on.

01
Catalog and taxonomy consolidation
  • Post-acquisition catalog consolidation
  • ERP migration taxonomy standardization
  • SKU rationalization and deduplication
  • Supplier taxonomy harmonization
  • Cross-system harmonization across ERP, PLM, SCM, and e-commerce
02
Semantic control plane deployment
Formal taxonomic structures encoded as governed, versioned assets. Not auto-generated tag clouds. Precision instruments for classification, retrieval, and analytics that hold consistently across every source and system.
03
Integration PMO partnership
  • Embedded with your integration team
  • Formal specifications and migration tooling
  • Governance frameworks built in from day one
  • Production-ready outputs, not advisory deliverables
  • 8 to 12 weeks to unified systems

See it in action

Caldwell Group is a PE-backed managed services business with three acquired divisions. Each division inherited its own data structures. Select a question to see what AI returns from unharmonized data versus a Hypericum semantic control layer.

Unharmonized Three legacy systems, no shared taxonomy
?

Select a question above to query both datasets simultaneously

Harmonized Canonical taxonomy — unified classification layer applied

Harmonized response will appear here

Self-assessment

How governed is your classification layer?

1

Taxonomy consistency

How consistently are your core entities — products, customers, cost centers — classified the same way across systems, business units, and data sources?

3
Varies significantly: each system or unit uses its own structure Fully consistent: one governed classification across all sources
2

Integration overhead

When you acquire a business, onboard a new system, or connect a new data source, how much manual mapping and reconciliation work does your team absorb before the data is usable?

3
Significant: every integration requires substantial bespoke work Minimal: new sources map cleanly to a governed standard
3

Analytics reliability

How much manual reconciliation does your team perform before cross-system or cross-entity analytics outputs are trusted enough to act on?

3
Significant reconciliation required before results are usable Consistently reliable: outputs are trusted without intervention
4

Analytical depth

How granular is your reporting at the cost line, product, and customer level? Can you compare vendor performance in detail, or target product selection, bundling, and customer segments with confidence?

3
Reporting is too aggregated to drive commercial decisions Granular, structured data supports detailed vendor, product, and segment analysis
5

AI readiness

How confident are you that the classification layer beneath your AI or analytics initiatives is governed well enough to produce reliable, auditable outputs at scale?

3
Not yet governed: AI is building on an unstructured base Fully governed: the classification layer is production-ready for AI

Readiness indicator

Move the sliders to generate your assessment.

Taxonomy consistency
3
Integration overhead
3
Analytics reliability
3
Analytical depth
3
AI readiness
3
Discuss your results
15 / 25
Assessing...

Client work

Representative engagements. Details anonymized.

Supply Chain / Logistics
Global rate taxonomy after a multi-entity acquisition program

A supply chain planning and services provider had acquired multiple companies across jurisdictions but lacked unified classification for rates, locations, and transport modes across air, sea, rail, and road.

Hypericum built a standardized rate taxonomy enabling consistent quoting, routing, and performance reporting across the combined entity.

30-second quote generation. Reduced mispricing and delivery failures.
IT Services
Service taxonomy to support AI-driven customer operations

A US-based IT services firm expanding rapidly needed to integrate AI-powered customer service across product lines and service tiers, but inconsistent taxonomy was producing unreliable responses.

Hypericum standardized the service taxonomy and knowledge base structure underpinning the AI layer, enabling consistent and accurate responses across all product categories.

Seamless integration between CRM, AI, and ticketing systems.

How it works

Productized delivery at a fixed, transparent price. Three stages, clear outputs at each. Every engagement begins with a Blueprint.

01
Blueprint
2 weeks · Fixed price
  • Map existing classification systems across all sources
  • Identify conflicts, gaps, and integration blockers
  • Deliver a designed specification for your semantic control layer
  • Integration points, governance structure, and phased implementation roadmap
02
Standardization
8 to 16 weeks
  • Formalize taxonomies with URIs and version control
  • Create mapping and validation rules
  • Migrate and validate data against governed specifications
03
Governance
Ongoing
  • Version control and change management
  • Quality monitoring and validation
  • New system and acquisition integration

Start here

Every engagement begins with a Semantic Control Layer Blueprint. Two weeks. A designed specification and implementation roadmap. Fixed price, scoped in advance.

Semantic Control Layer Blueprint
Fixed price · Delivered in two weeks · Scoped in advance
  • Current classification landscape mapped across all systems and sources
  • Conflicts, gaps, and integration blockers identified
  • Designed specification for your semantic control layer
  • Integration points and governance structure defined
  • Phased implementation roadmap with cost and timeline estimates

Deliverables agreed in writing before work begins. No ambiguity about what you receive.

Request an assessment

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