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Case studies

Three engagements, their constraints and their results.

Clients remain anonymised where consent to publish has not been obtained.

Demanding contexts

Dexlay works in sectors where security, compliance, resilience and performance are essential:

Engagement sector

01Retail and mass-market distribution

International retailer

Response

A governance overhaul, accelerated by the Dexlay frameworks.

3 months → 5 weeks

development cycle

3 MONTHS5 WEEKS

Challenge

A fragmented data ecosystem, GDPR constraints and a time to market too slow for new AI use cases.

The engagement

  1. 01.1

    Unified governance

    Databricks Unity Catalog put in place for a 360° view and granular security across the whole data estate.

  2. 01.2

    Dexlay accelerators deployed

    Automated ingestion and exposure frameworks, which shorten the development cycle.

    Automated backup and purge tools, to guarantee storage-zone hygiene.

  3. 01.3

    Data privacy

    Our proprietary tool for automated copying and anonymisation is switched on, from production to development: teams work on real data in a fully secured environment.

Impact

  • Technical teams and business teams fully in step
  • Native compliance and immediate data exploration for marketing and the supply chain
  • A development cycle brought down from three months to five weeks, and significantly reduced running costs

02Healthcare and clinical research

International clinical research laboratory

Response

A centralised clinical platform on Azure, with an AI predictive analytics capability.

Trials accelerated

by an AI that prioritises the critical signals

Challenge

Building a highly secure data platform on Azure, bringing together clinical trial data from across the world.

The engagement

  1. 02.1

    Audit and assessment

    A strategic review of the existing platforms to clarify costs, capability gaps and priority improvements, and to lay a secure, scalable Azure foundation.

  2. 02.2

    FinOps maturity and governance

    A single, consolidated view of cloud spend, which improves cost transparency and makes budgets enforceable: spend becomes predictable and controllable.

  3. 02.3

    AI predictive analytics

    Azure Databricks to unify data processing and Machine Learning, and to produce predictive analytics on clinical operations.

Impact

  • Trial operations accelerated by an AI that prioritises the critical signals
  • Cost transparency by study and by supplier, with budget control that reduces rework and prevents overruns
  • Significantly reduced study timelines

03Banking and financial services

Private bank

Response

Radical optimisation of costs and operational performance, on the existing platform.

30%

reduction in Azure and Databricks billing

−30%

Challenge

A data platform on Azure whose processing costs were spiralling, with critical latencies on regulatory reporting.

The engagement

  1. 03.1

    FinOps and performance audit

    A precision diagnosis of the Databricks clusters and Azure Data Factory pipelines, to identify the root cause of the costs and the optimisation strategy.

  2. 03.2

    Processing optimisation

    Compute logic redesigned and smart sizing policies applied: 40% less processing time.

  3. 03.3

    Cost rationalisation

    An immediate 30% reduction in Azure and Databricks billing through resource restructuring, with no loss of compute power.

Impact

  • A high-performing platform
  • Budgets under control
  • Greater agility for the risk management teams

That sector experience informs our decisions.

Contact

Your context will be different.

The performance, governance or industrialisation levers may still be comparable.

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