Data Transformation and AI - Intro
For context I am professionally an Integration/Interoperability Engineer that has worked for Sterling Commerce, IBM, HealthCurrent and NextGen Healthcare. Myself personally I am a serial entrepreneur and product developer that has created 5 internet based technology services from idea to market (2 sold and 3 failed to gain traction). I like to leverage a first-principles thinking approach of breaking down problems to their most basic components and rebuilding solutions from the ground up, rather than fumbling through existing industry assumptions. Long story short, this is me spitballing with CoPilot AI to brainstorm a potential new product ideas in an area that I'm intimately familiar with. If any of this interests you please feel free to reach out.
So here I am sitting around asking CoPilot if it was possible to create an any‑to‑any data transformation engine using AI. Basically hand any AI system a data packet including the source data, and a set of instructions and just sit back and watch the magic happen, rather than going through the age old task of mapping and coding. To the level of my inquiry, the answer is no; however, the conversation did get rather interesting so I continued to ask it to help me build one using AWS as a platform and the following are the result.
1) Is it possible to
create an any‑to‑any data transformation engine using AI?
Short answer: Yes — partially today, more fully over time — but
not as a fully autonomous “fire‑and‑forget” system.
Long answer:
AI can already do a large portion of what an any‑to‑any engine needs:
✅
Infer schemas from raw data
✅
Recognize semantic equivalence between fields
✅
Propose mappings and transformations
✅
Generate transformation logic (code, rules, configs)
✅
Validate outputs against known schemas
✅
Explain transformations in human terms
However, what we have today
is best described as:
AI‑assisted any‑to‑any
transformation, not AI‑driven
autonomous transformation
The reason is simple and
fundamental:
Transformation is not just
structural — it is semantic, contextual, and contractual.
AI can infer, suggest,
and generate, but it still needs:
- grounding via schemas, constraints, and examples
- human or system confirmation for ambiguous
semantics
- deterministic guarantees for regulated or mission‑critical
flows
So yes, it’s possible — but
the winning design is AI + deterministic engines, not AI alone.
2) What would an AI‑based
any‑to‑any transformation engine look like?
A realistic system would not
look like a single monolithic “AI mapper.”
It would look like a layered transformation pipeline, with AI used
surgically where it excels.
Here’s a practical reference
architecture.
🔷 A. Canonical Flow Overview
Input Data
↓
Structure & Schema
Inference (AI-assisted)
↓
Semantic Understanding &
Annotation (AI-assisted)
↓
Canonical Intermediate Model
(deterministic)
↓
Mapping & Transformation
Generation (AI-assisted)
↓
Validation & Constraint
Enforcement (deterministic)
↓
Output Serialization
The key design trick
is the canonical intermediate representation.
🔷 B. Step-by-Step Breakdown
1️ Input Understanding Layer
Purpose: Accept anything.
Supported inputs:
- HL7 v2/v3/FHIR
- XML / JSON / CSV
- EDI
- Fixed-width flat files
- Proprietary blobs
- Database extracts
AI helps with:
- Field boundary guessing (CSV, fixed-width)
- Data type inference
- Optional field detection
- Repeating group inference
- Relationship detection
💡 This is very similar to what you're already doing with HL7 schemas — except generalized.
2️ Semantic Annotation Layer (Where AI Shines)
This is the actual hard part.
Example:
JSON
{
"field": "PID.5.1",
"value": "CASE",
"semantic_concept": "patient.family_name"
}
Show more lines
AI pulls from:
- Field names
- Code system lookups
- Historical data patterns
- Sample payloads
- Domain embeddings (healthcare, finance, logistics)
This is where deterministic mapping engines fail and AI adds massive value.
3️ Canonical Intermediate Model (Critical)
Instead of mapping from A → B, you map:
A → Canonical → B
Think:
- A domain-aware “meta-model”
- Graph or tree-based, not flat
- Strong typing + versioning
- Explicit relationships (not implied positions)
In healthcare, this might resemble:
- Resources + references (FHIR-like)
- With relaxations for non-conformant inputs
This drastically reduces the N² mapping problem.
4️ Transformation Logic Generation
AI generates:
- Field mappings
- Conditional rules
- Lookups
- Normalizations
- Format conversions
- Code-system translations
Output forms:
- JavaScript
- SQL
- XSLT
- JSONata
- Mirth transformers
- Config-based rules
Example:
JavaScript
if (input.dob && input.dob.length === 8) {
output.dateOfBirth = parseYYYYMMDD(input.dob)
}
Show more lines
Important:
AI generates logic — but the runtime remains deterministic.
5️ Validation & Guarantees
This is not an AI job.
Handled by:
- Schema validation
- Constraint solvers
- Cardinality enforcement
- Referential integrity
- Business rule engines
Failures trigger:
- AI-driven explanations
- Suggested fixes
- Confidence scoring
6️ Human-in-the-Loop Refinement (Optional but Realistic)
Even elite systems need:
- Approval workflows
- Confidence thresholds
- Human review for new schemas
- Incremental learning
In fact, feedback becomes training data for better future mappings.
3) What are the
constraints of doing transformation this way?
This is the most important
question — because the constraints define whether this is a tool or a business.
⚠️ Constraint #1: Semantic Ambiguity Is Irreducible
Example:
JSON
"status":
"D"
Show more lines
Is "D":
- Deceased?
- Discharged?
- Deleted?
- Disabled?
AI can guess — but guessing
is unacceptable in regulated systems.
✅
Solution:
- Confidence scoring
- Required confirmations
- Explicit vocabulary bindings
⚠️ Constraint #2: Determinism Is Mandatory
AI is probabilistic.
Transformations must be deterministic.
This means:
- AI can generate rules
- AI cannot execute transformations directly
- Every output must be reproducible
✅
Solution:
- AI writes code/configs
- Deterministic engines execute them
⚠️ Constraint #3: Explainability & Auditability
You must answer:
- Why was this field mapped?
- What rule was applied?
- What changed between versions?
Black-box transformations are
unacceptable.
✅
Solution:
- AI produces structured rationale
- Store mapping provenance
- Version everything
⚠️ Constraint #4: Edge Cases Never End
The “last 5%” problem:
- Legacy systems
- Bad upstream data
- Contractual quirks
- “We always do it this way” logic
AI helps discover these
faster — but they never disappear.
⚠️ Constraint #5: Domain Depth Matters More Than Model Size
A general LLM < a smaller domain-trained
model.
Healthcare, finance,
logistics, etc., all require:
- Ontologies
- Code systems
- Regulatory awareness
- Institutional knowledge
✅
Solution:
- Retrieval-augmented generation (RAG)
- Domain-specific model tuning
- External knowledge graphs
⚠️ Constraint #6: Cost and Latency
Schema inference and semantic
mapping are expensive. You cannot do full AI inference on every message.
✅
Solution:
- AI at design time
- Deterministic execution at runtime
- Cache everything
Bottom Line
If I had to summarize it in
one sentence:
AI can dramatically reduce
the human effort needed to design and maintain transformations, but it cannot
fully eliminate the need for explicit models, constraints, and deterministic
execution.
Given your background in Mirth,
HL7, schema validation, and multi-tenant platforms, you’re actually in a perfect
position to build this:
- You already think in canonical models
- You understand the cost of ambiguity
- You’ve seen how brittle hand-built mappings are
Follow-up question for
you:
If you were to build this
first for one domain (healthcare, imaging, MPI, etc.), which part would
you target first:
- schema inference
- semantic mapping
- rule generation
- or canonical model design?
That choice determines
whether this becomes a tool, a platform, or a company.
Comments
Post a Comment