Accelerating Digital Transformation for a Mid-Sized Bank
INDUSTRY:
Banking
Executive summary
A mid-sized UK bank embarked on a platform modernisation to improve agility, resilience and customer experience. The destination was a composable, cloud-native BPaaS providing an integrated stack for digital channels, core banking, CRM and servicing, operated as a managed platform on public cloud.
Ennovision was engaged to own the data migration—covering strategy, detailed design and hands-on execution. Working jointly with the bank and the platform provider, we defined the canonical target model, engineered secure pipelines, executed multiple production-scale rehearsals, and delivered a calm, auditable cut-over with zero variance on material balances and minimal disruption for customers and colleagues.
The programme established durable foundations—governed data pipelines, lineage, reconciliations and defect triage practices—that continue to support change beyond Day-2
Client context and goals
The bank served retail and SME customers through web and mobile, with products spanning savings, mortgages and specialist lending. Years of incremental delivery had resulted in:
• Fragmented customer, product and ledger data across several systems.
• Point-to-point integrations and manual workarounds that made change slow and brittle.
• Time-consuming reconciliations that diverted operations from customer value.
The modernisation brief put data at the centre:
1. Migrate once, migrate right—ensure correctness, completeness and regulatory traceability.
2. Reduce operational risk—automate reconciliations, codify controls, and remove spreadsheet “glue”.
3. Enable future speed—establish canonical models and governed pipelines to accelerate product and reporting changes on the new platform.
4. Minimise disruption—guard customer experience and colleague workflows during the transition.
The target BPaaS combined a modern SaaS core banking system, enterprise CRM, digital channels and managed operational tooling on a public cloud foundation. Its capabilities strongly influenced target data structures, interfaces and operating procedures.
Scope of the migration
In scope:
• Customer & party: customers, contacts, KYC/AML flags, consents, communications preferences.
• Products & accounts: product catalogue, account master data, pricing attributes, term/notice rules.
• Balances & transactions: opening balances, accrued interest, schedules, transaction history per retention policy.
• Servicing & CRM: active cases, interactions and notes needed for in-flight continuity.
• Finance & regulatory: mappings to the general ledger and regulatory datasets.
Out of scope (deferred):
• Deep historical telemetry and legacy artefacts not required for servicing, finance or regulation.
• Obsolete products earmarked for closure or run-off
Strategy: prove correctness, then prove repeatability
Our approach rested on three principles:
1. Canonical first. Define a canonical target model aligned to the BPaaS platform constructs (core banking product/account schemas, CRM objects). This avoids one-off translation layers and simplifies future change.
2. Controls as code. Treat lineage, reconciliations and data quality (DQ) as first-class deliverables. If it matters to Finance, Risk or Audit, it must be automated and versioncontrolled.
3. Rehearse to confidence. Execute multiple dress rehearsals with production-sized data, converging on performance, reconciliation precision and cut-over timings.
Phased plan:
• Discovery & profiling — source inventory, critical field catalogue, data risks and dependencies.
• Target modelling & mapping — alignment to platform schemas, transformation rules, survivorship logic, gap analysis.
• Build & simulate — pipelines and tooling; synthetic/masked datasets for early cycles.
• Dress rehearsals — full end-to-end migrations into non-production environments; DQ and financial reconciliations. • Cut-over — controlled freeze, final deltas, validations, enablement.
• Hypercare — enhanced monitoring, side-by-side reporting and BAU handover.
Target architecture and tooling
Platforms.The provider operated the managed BPaaS on public cloud, integrating core banking, CRM and digital channels behind secure, rate-limited ingress. Our pipelines respected platform connectivity, security and interface patterns.
Data flow (high level):
1. Extract — change-data-capture (where available) or audited snapshots from legacy sources, with manifest tracking.
2. Stage — land encrypted files into a secure object store; enforce folder-level ACLs and object-level encryption.
3. Transform & validate — ELT pattern using SQL and Python; apply mapping rules to canonical tables; run DQ checks and conformance tests.
4. Load — ingest to target systems via bulk APIs and platform loaders, using idempotent batches and retry semantics.
5. Reconcile — compute and compare control totals for balances, counts and key attributes; raise exception worklists for investigation.
6. Publish evidence — store lineage graphs, control reports, hash manifests and approvals in a governed repository.
Tooling highlights:
• Versioned transformation code with peer review, automated unit tests and environment-agnostic configuration.
• DQ rules engine for required field coverage, referential integrity, allowable ranges, date windows and standardised formats (e.g., sort codes, IBANs where applicable).
• Reconciliation suite comparing ledger, sub-ledger and account balances; configurable tolerances for rounding and accrual.
• Synthetic data factory to protect PII during early rehearsals while preserving realistic distributions and edge cases
Data modelling and mapping
Customers & parties. We unified person and organisation representations and enforced one customer, one golden record, using deterministic and probabilistic matching. We carried forward consents and preferences and preserved KYC/AML risk flags with clear provenance.
Products & accounts. Legacy product catalogues were aligned to standardised product templates within the platform. Bespoke attributes were rationalised to platform equivalents (interest calculation, fee schedules, term/notice attributes). Where the platform offered extension fields or configuration rather than code, we used them to avoid re-introducing technical debt.
Balances & transactions. Opening balances were recomputed from source transactions and interest rules to eliminate historical drift. For lending products, we migrated in-flight schedules (next payment due, arrears status) and preserved transaction ordering for audit and customer experience.
Servicing & CRM. Active cases, complaints and tasks were mapped to CRM objects, preserving assignment, SLA clocks, attachments and notes so day-one service continued seamlessly.
Finance & reporting. GL mapping tables routed product and posting types to correct ledger accounts. We prepared curated joins required for regulatory returns supported by the new platform, ensuring that audit trail and lineage were intact from the first report cycle.
Data quality, cleansing and enrichment
• Encryption everywhere — TLS in transit; envelope encryption at rest; strict key management and rotation.
• Least-privilege — tight IAM roles for pipelines and operators; short-lived credentials; break-glass procedures.
• Secrets hygiene — centralised secret storage; no plaintext secrets in repositories; signed artefacts.
• Masking & tokenisation — early rehearsals used masked or synthetic data; production rehearsals handled PII under enhanced controls and monitoring.
• Consent & retention — migration respected customer consents and retention schedules; expired or out-of-policy data was not imported.
• Auditability — every load produced control totals, hash manifests and row-level exception files, enabling independent verification by Finance, Risk and Internal Audit.
Rehearsals: making migration boring (in the best way)
1. Functional rehearsal — validate mappings, balances and end-to-end flow; surface data oddities early.
2. Performance & volume rehearsal — production-sized data; tune batch sizes, parallelism and API throttling to meet the cut-over window.
3. Operational rehearsal — execute the cut-over run-book and war-room protocols; time every step; measure time-to-green for reconciliations.
Each rehearsal produced a variance report by table, field and control; defects were triaged and fixed for the next iteration. Go-live criteria were explicit: zero variance on principal balances and accrued interest; DQ exceptions within agreed ceilings with compensating actions where appropriate.
Cut-over approach
A prolonged freeze was unacceptable due to daily customer activity. We therefore executed a phased cut-over with CDC catch-up:
• T-7 to T-1 — incremental loads nightly; customer communications prepared; front-line scripts rehearsed.
• T-1 — short freeze; final delta extract; compute opening balances; pre-validate control totals.
• T-0 (weekend window) — execute final loads; run automated reconciliations; Finance sign-off; enable the new channels.
• T+1 to T+14 (hypercare) — enhanced monitoring; side-by-side reporting; accelerated defect resolution and BAU handover.
Rollback criteria were defined and tested (e.g., reconciliation failures on material balances, or platform unavailability beyond an agreed threshold). Rollback was not required.
Integration with the BPaaS platform
• Core banking and CRM loads conformed to platform ingestion patterns (bulk APIs, schema constraints, idempotency keys and throttling guidance).
• Digital channels were validated to render migrated data accurately on first login, avoiding day-one customer confusion.
• Operations tooling (cases, tasks, knowledge) was seeded so in-flight service continued without re-keying.
• Platform guardrails (cloud security posture, environment separation, monitoring) were followed exactly, reducing variance between rehearsal and live.
Results
• Correct to the penny — principal balances and accrued interest reconciled at zero variance at go-live; minor non-financial exceptions were cleared within hypercare.
• Minimal disruption — customers accessed channels as planned; branch and contact centre staff continued cases in the new CRM with no data loss.
• Audit-ready evidence — lineage, control totals, exception logs and approvals formed a complete audit pack, simplifying assurance.
• Faster change post-migration — canonical models and governed pipelines enabled quicker product tweaks, reporting improvements and analytics initiatives.
What made the difference
1. Platform-aware design — modelling directly to standard platform constructs avoided brittle translation layers and reduced post-go-live surprises.
2. Controls as code — automating reconciliations, DQ and lineage created repeatability and confidence.
3. Relentless rehearsal — each full dress rehearsal reduced unknowns and accelerated sign-offs at cut-over.
4. One team — Ennovision engineers, the bank’s Finance/Risk/Ops leads and the platform provider worked from a single change ledger and definition of done.
5. Customer day-one focus — we designed for user-perceived continuity (transaction ordering, case histories, communications) to avoid needless contact spikes.
Lessons for banks moving to managed, composable platforms
• Start with the canon — a canonical model aligned to platform building blocks is the shortest path from legacy sprawl to clarity.
• Agree non-negotiables early — decide which balances must reconcile to zero and which histories are mandatory; have Finance set the bar.
• Treat CRM as first-class — service continuity depends on cases and context, not just accounts and balances.
• Design for dual-run and CDC — your cut-over window will thank you.
• Engineer the evidence — lineage, manifests and control totals defuse debates and satisfy auditors.
Roadmap (post-migration)
1. Historical deepening — back-load extended history for analytics where cost–benefit is positive.
2. DQ observability — embed continuous DQ monitoring into BAU with automated ticketing for drift.
3. Regulatory data services — extend curated datasets to streamline returns and stress testing.
4. Event-stream enrichment — publish domain events to power near real-time insights and personalisation (with strong privacy guardrails).
5. Decommissioning & savings — retire legacy warehouses and interfaces to lock in TCO reductions.
Why Ennovision
Ennovision specialises in cloud engineering, application engineering and data & AI for regulated industries. On this programme we were accountable for end-to-end data migration—from strategy and modelling to pipeline build, rehearsals and cut-over—onto a composable, cloud-native BPaaS. Our approach combined platform fluency, financial-grade reconciliations and automation-heavy delivery, resulting in a migration that was accurate, auditable and calm.