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Datalloy

Case Studies

Example Engagements

Four representative scenarios showing how Datalloy approaches a data problem from source system to reported number.

These are illustrative examples. They describe the approach Datalloy takes rather than named client projects, and they deliberately contain no client names, logos or performance figures. Published, client-approved case studies will replace them as they become available.

Examples

Problem, solution, outcome

Each example follows the same structure — what was wrong, what was built and what changed as a result.

  • ManufacturingData EngineeringData WarehousingBusiness IntelligenceIllustrative example

    Manufacturing Data Warehouse

    Problem

    Multiple ERP systems across plants produced inconsistent reporting. The same product and cost centre existed under different codes in each system, so group-level figures had to be assembled manually and rarely reconciled.

    Solution

    • Centralised data ingestion from each ERP instance
    • Standardisation and cleansing of product, plant and cost centre codes
    • Enterprise data warehouse with conformed dimensions
    • Fact and dimension modelling with Slowly Changing Dimensions
    • Power BI semantic model with governed measure definitions

    Outcome

    • Centralised reporting across all plants
    • Consistent product and cost centre definitions
    • Group reporting produced from one modelled source
    • Manual consolidation work removed from the month-end process

    Illustrative example — representative of the work Datalloy does, not an actual client engagement.

  • DistributionData MigrationData EngineeringData WarehousingIllustrative example

    ERP Migration Reporting Continuity

    Problem

    A move to a new ERP platform put historical reporting at risk. Legacy data structures were not compatible with the new system, and the business needed to compare performance across the cut-over point.

    Solution

    • Mapping of legacy structures to the target model
    • Historical data extraction and archival into the data lake
    • Reconciliation between legacy and target records
    • Unified warehouse model spanning both source generations
    • Parallel-run validation before decommissioning legacy reporting

    Outcome

    • Historical reporting preserved beyond the migration
    • Like-for-like comparison across the cut-over
    • A single reporting model covering both platforms
    • Legacy reporting retired with a documented validation trail

    Illustrative example — representative of the work Datalloy does, not an actual client engagement.

  • FinanceData EngineeringData QualityBusiness IntelligenceIllustrative example

    Finance Reporting Automation

    Problem

    Month-end reporting was assembled from exports and spreadsheets. Figures could not be traced back to source, and every restatement required the previous version to be rebuilt by hand.

    Solution

    • Automated extraction from ledger and operational systems
    • Validation and reconciliation rules applied inside the pipeline
    • Warehouse layer holding period history
    • Governed measure definitions agreed with finance
    • Scheduled Power BI refresh with failure alerting

    Outcome

    • Reported figures traceable back to source records
    • Reconciliation exceptions surfaced before reporting, not after
    • Consistent measure definitions across finance reports
    • Spreadsheet assembly removed from the reporting cycle

    Illustrative example — representative of the work Datalloy does, not an actual client engagement.

  • LogisticsCloud DataData EngineeringBusiness IntelligenceIllustrative example

    Operational BI Platform

    Problem

    Carrier files, telematics feeds and internal systems each arrived in a different format on a different schedule. Depot performance was measured inconsistently, and there was no reliable history to analyse trends.

    Solution

    • Azure Data Factory ingestion for files, APIs and databases
    • Data lake zones for raw, curated and archived data
    • Standardised shipment and movement modelling
    • Service level and cost measures defined once in the semantic model
    • Operational dashboards for depot and network views

    Outcome

    • One consistent definition of delivery performance
    • Historical trend analysis made possible
    • Manual file handling replaced by monitored pipelines
    • Depot and network reporting from the same model

    Illustrative example — representative of the work Datalloy does, not an actual client engagement.

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