high-value, high-pain market.
Oracle-to-SAP and Oracle-to-IBM migrations are notorious for cost overruns, and the three problems you've named (ETL cost, eval datasets, data selectors) are exactly where ArcXA has native leverage. Here's the full breakdown:
ArcXA in Oracle → SAP / IBM Migration
The Pain ArcXA Solves
Enterprise migrations from Oracle to SAP S/4HANA or IBM Db2/Power typically fail or overrun for three reasons:
- Manual ETL is hand-coded, schema by schema, at enormous labor cost
- No ground truth exists to validate whether migrated data is correct
- Data selection — deciding what to move, when, and in what order — is done by gut feel or expensive consultants
ArcXA attacks all three simultaneously.
1. Reducing Manual ETL Cost
Problem: Traditional migration ETL is written by hand — developers reverse-engineer Oracle schemas, map them to SAP/IBM equivalents, write transformation logic, and pray it holds when the source data has anomalies. At scale this costs millions.
ArcXA Migration Mapping Accelerates and Insures the project:
- Automated schema discovery — ArcXA scans Oracle source systems and auto-generates a complete schema inventory including tables, views, stored procedures, dependencies, and data types. No manual reverse engineering.
- Lineage-aware transformation mapping — because ArcXA tracks how data flows between fields and tables, it can auto-propose transformation rules rather than requiring developers to infer them.
- Governance rule inheritance — data classification, PII tagging, and access controls discovered in Oracle are automatically carried forward into the target SAP/IBM schema, eliminating a separate compliance mapping exercise.
- Anomaly pre-detection — before ETL runs, ArcXA flags data quality issues (nulls, type mismatches, referential integrity breaks) that would otherwise surface as pipeline failures mid-migration.
Cost impact: Reduces ETL development time by eliminating discovery, manual mapping, and rework cycles. Migrations that take 18 months compress significantly when the schema intelligence layer is automated.
2. Building Evaluation Datasets for Migration Validation
The problem: How do you know the migrated data is correct? Most teams do spot-checks. That's not good enough for financial, HR, or supply chain data moving from Oracle into SAP.
How ArcXA helps:
- Ground truth capture — ArcXA takes a governed snapshot of the Oracle source at migration time — schema state, data values, lineage, relationships — creating a verifiable baseline.
- Transformation pair generation — every source record mapped to a target record becomes an input/output eval pair. ArcXA generates these automatically from its migration intelligence layer.
- Multi-hop validation — for data that touches multiple systems (Oracle → staging → SAP BW → S/4HANA), ArcXA's KGNN tracks the full chain, enabling end-to-end correctness checks, not just point-to-point.
- Regression eval sets — after go-live, ArcXA's eval dataset becomes the regression suite. Any future schema change in SAP or IBM is tested against the original Oracle ground truth.
- Agentic AI validation — if you deploy AI agents to assist with migration QA, ArcXA's eval sets become the harness those agents are tested against — closing the loop with the agentic AI use case already established for RocketWorx.
Cost impact: Replaces manual QA sampling with systematic, automated validation. Catches data corruption before it becomes a production incident — which in SAP ERP environments can mean financial misstatement or supply chain failure.
3. Data Selectors — What to Move, When, and How
The problem: Not all Oracle data needs to move. Historical transactional data, archived records, deprecated tables, and shadow IT data lakes all create noise. Poor data selection inflates migration scope and cost.
How ArcXA helps:
- Usage frequency analysis — ArcXA identifies which Oracle tables and fields are actively queried vs. dormant, enabling intelligent tiering: migrate hot data first, archive cold data, discard unused data.
- Dependency mapping — before selecting a table for migration, ArcXA maps all upstream and downstream dependencies. You can't safely move an Oracle fact table without knowing which 40 views and 12 stored procedures depend on it. ArcXA surfaces this automatically.
- Business criticality scoring — using the KGNN layer, ArcXA scores entities by their centrality in the data graph. High-centrality nodes (master data, reference tables, shared keys) are prioritized in migration waves.
- Regulatory data selectors — for migrations involving regulated data (HIPAA, ITAR, FedRAMP, SOX), ArcXA's governance layer automatically tags data that requires special handling, sequencing, or exclusion from certain target environments.
- Wave planning support — ArcXA's dependency and usage intelligence feeds directly into migration wave planning — giving program managers a data-driven basis for deciding what goes in Wave 1, Wave 2, and Wave 3 rather than relying on consultant judgment.
Cost impact: Reduces migration scope by eliminating unnecessary data movement. Reduces risk by ensuring dependencies are respected in sequencing. Replaces expensive consultant-led data profiling exercises.
Compound Effect
The real power is when all three work together:
ArcXA discovers Oracle schema
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Data selectors determine scope and wave order
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Transformation mappings auto-generated for ETL
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Ground truth eval sets built from source snapshot
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ETL runs with anomaly pre-detection active
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Post-migration validation against eval sets
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Regression suite lives on for ongoing SAP/IBM governance
This turns a one-time migration project into a continuous governance capability — ArcXA doesn't go dark after go-live. It becomes the system of record for data lineage and quality in the new SAP/IBM environment.
Competitive Positioning
Want me to build this into a use case one-pager, an SI partner brief (targeting Accenture/Deloitte Oracle migration practices), or a technical capability statement for IBM and SAP ecosystem selling?
Core Architecture (xGT, KGNN)
RocketWorx is a composite platform assembled from four integrated components:
1. RocketGraph — the graph analytics engine, running natively on IBM Power11. Provides the real-time relationship mapping, multi-hop traversal, and entity resolution that powers the intelligence layer. Where Palantir builds its graph on cloud infrastructure, RocketWorx runs graph-native on Big Iron — dramatically faster for large structured datasets.
2. Equitus AI / ArcXA — the data governance and migration intelligence layer. Handles schema discovery, lineage, KGNN knowledge graph construction, and runtime governance enforcement. This is what makes RocketWorx a System of Action rather than just an analytics viewer.
3. ThreatWorx — the threat intelligence and cybersecurity module. Feeds adversarial signals, anomaly detection, and ICAM (Identity, Credential, and Access Management) into the platform. Closes the loop between data intelligence and operational security.
4. IBM Power11 — the infrastructure substrate. Not a cloud, not a hyperscaler — on-premise, air-gappable, FIPS-compliant, and built for mission-critical workloads. This is the key differentiator against Palantir, which is fundamentally a cloud/SaaS play.
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