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Data & AI Practice

Data Modernization Services

Data modernization replaces ageing warehouses, siloed marts and manual extracts with a cloud native architecture that is cheaper to run, faster to change and ready for analytics and AI. Erpvora plans and delivers these programmes with a bias toward low risk, incremental migration rather than a single high stakes cutover.

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Move off legacy data warehouses and fragmented stores to a modern, cloud native data architecture without disrupting the business.

The business challenge

Legacy data platforms carry real cost and real risk. Licensing and hardware are expensive, specialist skills are scarce, and years of undocumented logic sit inside stored procedures and scheduler jobs that few people still understand. Every change is slow and every outage is frightening.

Meanwhile the business wants self service analytics, near real time insight and AI features that the old platform cannot support. Leaders feel pressure to modernize but fear a big bang migration that could break reporting the company runs on. Inaction and reckless speed are both expensive.

Our approach

We start by understanding what the current platform actually does, not what documentation claims. We profile usage, identify the reports and jobs that matter, and retire what no longer earns its place rather than carrying it forward.

We then migrate in slices. Workloads move to the target architecture in prioritized waves, running in parallel and reconciled against the legacy system until confidence is earned. This keeps the business running while the estate shrinks on the old platform and grows on the new one.

Capabilities

  • Current state assessment, usage profiling and workload prioritization
  • Target data architecture design on cloud native platforms
  • Migration of warehouses, marts, pipelines and reporting logic
  • Parallel run and reconciliation between legacy and target
  • Decommissioning of retired platforms and redundant datasets
  • Cost modelling and governance for the modern estate

How we deliver

  1. 01

    Assess

    We profile the existing estate, map dependencies and usage, and identify what to migrate, refactor or retire.

  2. 02

    Design target

    We define the modern architecture, security model and cost guardrails aligned to how the business uses data.

  3. 03

    Prioritize waves

    We group workloads into migration waves ordered by value and risk, starting with contained, high confidence candidates.

  4. 04

    Migrate and reconcile

    We move each wave, run it in parallel and reconcile outputs against the legacy system before switching consumers over.

  5. 05

    Decommission

    We retire legacy components only once consumers are stable, capturing savings and reducing the attack surface.

Typical use cases

  • Retiring an on premise enterprise data warehouse near end of life
  • Consolidating scattered departmental marts into one governed platform
  • Reducing licensing and infrastructure cost through cloud native storage and compute
  • Unlocking self service analytics that the legacy platform could not support
  • Refactoring opaque stored procedure logic into tested, documented transformations
  • Preparing a modern foundation ahead of an AI or advanced analytics programme

Business impact

  • Lower and more predictable running cost through elastic compute
  • Faster delivery of new datasets and reports
  • Reduced dependence on scarce legacy skills
  • Migration risk contained through parallel run and reconciliation
  • A platform ready for analytics, machine learning and AI
  • Cleaner, documented logic replacing undocumented legacy code

Frequently asked questions

Is a big bang migration ever the right choice?

Rarely for critical reporting estates. We strongly favour incremental, parallel run migration because it lets the business keep operating and reconcile results before committing to a cutover.

What happens to undocumented legacy logic?

We profile actual usage and reverse engineer the logic that matters, rewriting it as tested transformations. Logic that drives nothing in practice is retired rather than migrated.

How do you control cloud cost during modernization?

We model workload cost up front, set guardrails and monitoring, and tune storage and compute so the modern estate is cheaper to run than the one it replaces.

Which target platform should we choose?

We stay platform neutral and recommend based on your existing cloud, skills, workloads and commercial context rather than a fixed preference.