Data & BI · Retail & FMCG
Multi-Store Enterprise Data Centralisation
Participation in a retail system conversion consolidating store-level data into a central environment.
Operational· pending verification
Alex
02Data & Business Intelligence
We establish whether the numbers can be trusted, fix the sources where they can't, and build reporting that management can act on.
Led by Alex
The reports exist. Nobody fully trusts them.
- Same product, different barcodes, costs and descriptions across systems.
- Margin reports disagree with supplier invoices.
- Reconciliation lives in personal spreadsheets nobody else can follow.
- Exceptions are discovered at month-end — or not at all.
Solution capabilities
Business Intelligence & Analytics
- Power BI dashboards & Power Query models
- KPI development
- Excel-based analytics
- Pricing & gross-profit analysis
- Multi-location operational reporting
- Management decision-support dashboards
Data Engineering, Quality & Governance
- Data-quality assessments
- Source-system reconciliation
- Master-data standardisation
- Duplicate identification
- Business-rule validation & exception reporting
- Data lineage documentation
- Data governance frameworks
- Production ETL / ELT pipelinesAssessed per project
Example use cases
- Product master-data reconciliation across supplier, DC and store systems
- Promotional pricing and supplier invoice analysis
- Gross-profit and claims investigation
- Multi-store performance reporting
Implementation approach
- 01
Source inventory
Map every source, its owner, its refresh cycle and the identifiers that join it to everything else.
- 02
Profiling
Measure completeness, uniqueness, validity and consistency before any dashboard is drawn.
- 03
Rules & reconciliation
Codify business rules, reconcile sources, and route exceptions to accountable owners.
- 04
Reporting
Model KPIs on validated data, with lineage visible to the people relying on them.
Example architecture
Trusted reporting pipeline — illustrative, adapted per engagement.
Supplier data
DC records
Retail ERP
Profiling
Validation rules
Reconciliation
Power BI
Applicable technologies
- Power BI
- Power Query
- Excel
- SharePoint
- Power Automate
Expected categories of benefit
Outcomes depend on each organisation's starting point; we agree measurable targets during discovery rather than promising them in advance.
- Clear visibility of which data is reliable — and which isn't
- Documented, repeatable reconciliation instead of personal spreadsheets
- Exceptions surfaced to owners rather than discovered late
- Reporting with traceable lineage
Scope notes
- Production-grade ETL/ELT platforms are an expanded capability assessed per engagement, not an established delivery history.
- SQL, Python/Pandas and advanced DAX are active development areas.
Related work
Data & BI · Retail & FMCG
Multi-Stage Product Master-Data Reconciliation
Reconciling product identifiers, pack sizes and costs across supplier, DC and retail records.
Operational· pending verificationAlexData & BI · Retail & FMCG
Promotional Pricing & Invoice Analysis
Comparing promotional selling prices and supplier invoice costs to expose margin exposure.
Operational· pending verificationAlexData & BI · Retail & FMCG
Retail Gross Profit & Claims Investigation
Tracing margin variances to cost, pricing and claim causes.
Operational· pending verificationAlexData & BI · Retail & FMCG
FMCG Data Cleansing & Enrichment Framework
Repeatable rules for cleansing and enriching product data — demonstrated here with synthetic data.
Prototype / ExperimentAlex + Jakes
Let’s build something that works.
Whether you need a custom application, better visibility into your data, connected operational systems, or a practical automation strategy, we can help define the right solution.