Data migration without the debt that follows every programme
Data migration succeeds when the target platform receives certified data, not when the runtime finishes on schedule. BCS designs and executes migrations onto Snowflake, Databricks, Microsoft Fabric, BigQuery, Azure, AWS, and SAP Datasphere with deKorvai validation at every stage, so reconciliation is a sign-off step, not a recovery exercise.
of data migration projects fail or significantly exceed budget and timeline
of organisational data is integrated across enterprise systems on average
monthly cost of poor data pipeline quality in mid-market enterprises
Three things BCS validates before any data is moved
Most migration programmes start at the runtime: pick the tool, build the pipeline, run the load. BCS data migration begins at the source data, profiling quality and reconciling business rules before a single record is moved. The cutover that arrives clean is the cutover designed for it.
Measure before designing
deKorvai quality baseline established before any architecture decision is made. Every recommendation is grounded in measured evidence from the current data estate, not assumptions from stakeholder interviews.
Automate from the first sprint
Symphony automation scope identified and embedded in the delivery roadmap during the engagement itself, not proposed as a separate follow-on programme after delivery concludes.
Govern from day one
Anugal access governance and data classification policies are designed as part of the solution architecture and active from the first production dataset, not retrofitted after the platform is in use.
4 reasons data migrations produce dirty targets
Most organisations underestimate the source data quality problem before migration begins. 83% of projects fail because they move the data first and discover the quality issues second.
No source data profile before migration starts
Migrations begin without a baseline assessment of source data quality. Nulls, duplicates, encoding inconsistencies, and referential integrity violations are discovered in the target environment during testing, not in the source before extraction begins. Fixing source quality issues costs 5–10× more when discovered post-migration.
Transformation rules applied without validation gates
ETL transformations are written and executed without quality checks at each step. A transformation that silently drops records or introduces type mismatches propagates corrupted data through every downstream stage. Business users discover the problem when analytics results diverge from source system reports after cutover, at which point rollback is expensive.
Reconciliation is a spreadsheet exercise, not a system control
Post-migration reconciliation relies on manual record count comparisons in spreadsheets. These checks miss semantic differences: records that migrated but with incorrect values, aggregates that differ due to rounding rule changes, and business entities split or merged during transformation. The manual check passes while the business data remains wrong.
Integration is treated as a migration phase, not an ongoing discipline
Organisations migrate data and then treat integration as complete. New sources, updated schemas, and evolving business rules create ongoing integration debt. Without continuous pipeline monitoring, integration drift goes undetected until a business-critical process fails. An integrated data estate requires ongoing deKorvai monitoring, not a 1-time migration sign-off.
What the migration and integration engagement delivers
Target platform receives certified-clean data
deKorvai quality gates validate every record at extraction, transformation, and load. The target system inherits a clean data estate, not a migrated version of the source quality problems.
Cutover executed without business disruption
Rollback procedures, parallel-run schedules, and cutover criteria are defined before migration begins. Business operations continue during migration, with cutover triggered only after reconciliation confirms completeness.
Integration pipelines monitored continuously post-migration
Migration completion is not the end of the engagement. deKorvai monitors integrated pipelines continuously, catching schema drift, volume anomalies, and quality regressions before business processes are affected.
SAP and cloud platform migrations from a single team
Migrations from SAP ECC or BW to S/4HANA, Datasphere, Azure, AWS, or GCP are handled by BCS engineers with expertise across both the SAP and cloud layers, eliminating handoff risk at platform boundaries.
Fragmented source systems unified on a single target
Organisations with data spread across multiple ERPs, CRMs, and operational databases receive a unified integration layer on the target platform, with a single governed data model replacing the fragmented source estate.
Migration programme delivered against a fixed scope and schedule
BCS migration programmes are scoped against the deKorvai source profile, not against assumptions. Scope is defined by actual data volumes and quality findings, so timeline estimates reflect the real migration rather than the planned one.
How does BCS deliver data migration and integration?
Data migration succeeds when the data foundation is treated as the deliverable, not the migration runtime. BCS validates data integrity at every stage, designs the integration architecture before the build, and operates the new platform from day one of cutover.
Source and Target Assessment
BCS profiles source data, target data model, and the gap between them. Data quality, volumes, latency tolerance, and regulatory requirements are documented before migration design begins.
Architecture and Pattern Design
The integration architecture is designed: ETL or ELT, batch or streaming, CDC or full refresh per object. Pattern choices are documented against business volumetrics, not vendor defaults.
Pipeline Build and Validation
Pipelines are built using BCS reusable patterns, with deKorvai validation at every transformation stage. Source-to-target reconciliation runs automatically. Failed records are routed to a defined remediation queue.
Cutover Execution
BCS executes cutover with structured fallback plans, parallel-run validation, and reconciliation sign-off before legacy systems are retired. Migration windows are tested in dress rehearsals, not improvised on the night.
Operations and Optimisation
BCS operates the new data integration estate with Symphony-orchestrated runbooks and deKorvai anomaly detection. Performance tuning is continuous, not a follow-on engagement.
What BCS delivers across migration and integration programmes
BCS migration and integration capabilities span the full programme lifecycle, from source assessment through to post-cutover pipeline monitoring across SAP, cloud, and on-premise environments.
Source Data Profiling
Automated deKorvai profiling across all source systems before migration scoping. Completeness, uniqueness, validity, and referential integrity assessments across every table and field in scope, with prioritised remediation recommendations.
ETL Pipeline Design and Build
Migration pipeline architecture and build using Azure Data Factory, AWS DMS, SAP BODS, Talend, or Informatica matched to the source and target environment. Transformation rules documented against business glossary terms and validated against source profiling output.
deKorvai Quality Gate Integration
Automated quality checks at every pipeline stage: extraction completeness, transformation rule validation, load reconciliation, and business-layer aggregate comparison. Quality failures halt the pipeline and route exceptions to the resolution workflow before downstream processing continues.
SAP Data Migration
Migrations from SAP ECC, BW, and legacy SAP landscapes to S/4HANA, SAP Datasphere, and cloud platforms. BCS SAP expertise covers ABAP data extraction, SAP BODS pipeline design, and Datasphere integration layer configuration alongside the standard cloud migration toolchain.
Cloud-to-Cloud and On-Premise-to-Cloud Migration
Migration programmes covering on-premise to Azure, AWS, or GCP, cloud-to-cloud transfers between hyperscalers, and hybrid architectures where some workloads remain on-premise. Each pattern has specific network, security, and orchestration requirements addressed in the migration architecture design.
Real-Time Integration and API-Based Connectivity
Event-driven and API-based integration patterns for real-time data flows between operational systems and the data platform. Kafka, Azure Event Hub, AWS Kinesis, and REST API integration alongside batch ETL, with deKorvai monitoring across both batch and real-time pipelines.
Rollback Architecture and Cutover Management
Rollback procedures, parallel-run schedules, and cutover decision criteria designed before migration begins. Cutover is managed against measurable thresholds confirmed on the deKorvai reconciliation dashboard, not against project calendar dates. Rollback can be triggered at any stage without data loss on the source system.
Master Data Management Integration
Master data consolidation during migration programmes: customer, product, vendor, and asset master records deduplicated and standardised across source systems before loading to the target. Survivorship rules defined against business requirements and validated through the deKorvai quality framework.
Post-Migration Pipeline Monitoring
Ongoing deKorvai monitoring of integrated pipelines after migration cutover. Schema changes, volume anomalies, freshness failures, and quality regressions are detected at the pipeline stage, not when business reports produce unexpected results. The monitoring baseline established during migration transfers directly to the managed service.
The platforms that govern data movement from first extract to final validation
Migration Orchestration and Cutover Automation
Symphony
Symphony orchestrates migration sequences across extract, transform, load, and validation stages with dependency-aware execution. Cutover sequences run as governed automation, eliminating the coordination overhead that causes migration delays.
- Extract, transform, and load stage sequencing with dependency enforcement
- Cutover sequence automation replacing manual migration runbooks
- Automated rollback execution when migration validation gates fail
- Cross-system synchronisation coordination across migration wave boundaries
Data Integrity and Transformation Validation
deKorvai
deKorvai validates data integrity at every migration stage, comparing source and target record counts, schema conformance, and business rule adherence. Transformation errors are caught before data reaches the target environment.
- Source-to-target record count and schema conformance validation at each stage
- Business rule adherence verification for transformed datasets before load
- Post-migration data integrity confirmation before cutover sign-off
- Transformation error detection with root-cause identification for remediation
Migration Access and Data Security Governance
Anugal
Anugal governs access to source systems, migration tooling, and target environments throughout the migration programme. Contractors and migration teams access only the systems their current migration wave requires.
- Wave-scoped access to source and target systems throughout migration phases
- Migration tool access governance for contractors and external migration teams
- Automated access revocation on wave completion without manual cleanup
- Data transfer audit trail capturing every extract and load action
Frequently Asked Questions
Refer to this section for answers to frequently asked questions related to data migration and integration.
ETL versus ELT, which approach fits modern data platforms?
Cloud data warehouse, data lake, or lakehouse, what is the right choice?
How does BCS validate data integrity during migration?
What is the role of CDC (change data capture) in modern data architecture?
Can the data foundation be migrated without disrupting business operations?
What makes BCS different from every other data migration consultancy?
BCS has executed data migration programmes across SAP, Azure, AWS, and GCP for enterprise clients in manufacturing, financial services, and healthcare. Across ten years of migration delivery, deKorvai quality validation has been embedded in every programme, replacing the post-migration audit with continuous gate control.
Quality gates in the pipeline, not audits after it runs
deKorvai validation is embedded in the migration pipeline itself. Quality failures halt the pipeline at the point of failure, before bad data propagates. Issues are caught and resolved during migration, when correction cost is lowest.
Cutover is approved by data, not by project schedule
Cutover criteria are defined as measurable deKorvai thresholds before migration begins. Timeline pressure does not override the quality gate: cutover is gated on data confirmation, not calendar dates.
SAP and cloud in the same migration team
Migration programmes spanning SAP ECC or BW and cloud data platforms require expertise in both the SAP extraction layer and the cloud target. BCS migration teams hold both, eliminating handoff risk at the platform boundary.
Access governance active from day one on the target
Anugal access policies are defined during migration design and enforced from cutover day. Migrated data does not inherit the access control debt of the source system.
Rollback is designed before migration begins, not improvised during failure
Rollback procedures, parallel-run windows, and rollback trigger criteria are defined before a single record moves. Source systems are maintained in a rollback-capable state until deKorvai confirms target completeness.
Ready to migrate without the quality debt?
BCS migration programmes begin with a deKorvai source profile that establishes the real quality baseline before scoping begins. Book an initial migration assessment to understand the scope, risks, and timeline for the current source estate.