Data engineering
Data harmonization
Examples of projects:
Unifying retailer POS feeds across 18 markets:
- Built a harmonization pipeline that ingests weekly data from over 70 retailers, each with different formats, hierarchies, and refresh cadences.
- Applied rule-based mappings and ML-based schema matching to standardize item, store, and promo hierarchies.
- Integrated with the client’s cloud data lake, auto-updating golden data layer for downstream analytics and planning.
Merging loyalty, panel, and DTC data into unified shopper ID:
- Engineered a persistent ID graph combining hashed emails, cookies, in-store card IDs, and transaction patterns.
- De-duplicated millions of records using fuzzy matching, confidence scoring, and privacy-respecting PII reconciliation.
- Enabled unified shopper journey analytics across DTC, retail, and syndicated channels.
Standardizing product hierarchies across brands and markets:
- Created a cross-market PIM alignment engine using NLP and fuzzy clustering to map SKUs to standardised brand/category/facing roles.
- Incorporated regulatory, language, and pack variant differences across the EU, LATAM, and ANZ.
- Resulted in a harmonized brand hierarchy compatible with both internal BI and syndicated vendor platforms.
Value we have created:
98.7% mapping accuracy across retailers
Our harmonization pipelines achieved near-complete field match across 1.2B rows of POS data from 78 different retailer files.
4x acceleration in data refresh cycles
Reduced POS-to-insights lag from 7 days to under 36 hours across all major markets.
-80% time spent on data cleansing
Freed up analytics teams by eliminating manual reconciliation for weekly reports.
+100% increase in shopper match rate
Improved match rates for DTC and retailer datasets, enabling personalized targeting on 2x more contacts.
Global roll-out in 3 months
Our standardized harmonization framework enabled the client to expand into 6 new markets without re-engineering.
Why our data harmonization is scientifically better
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Retail-aware schema matching:
- We don’t rely on name-matching alone — we model metadata like unit sales, promo tags, and historical roles to resolve ambiguities.
- This allows us to distinguish, for example, between a seasonal sub-variant and a core pack with similar names.
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ML-augmented hierarchy mapping:
- Our system learns from corrections and applies context-aware mapping based on known relationships between brands, SKUs, and retailers.
- Fuzzy clustering and active learning ensure the system improves with every cycle, not just at onboarding.
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Designed for shopper-centric use cases:
- Harmonization isn’t just about rows — we build around shopper journeys, pack transitions, and substitution logic.
- Perfect for businesses trying to understand performance across retailers, banners, and omnichannel touchpoints.
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Built-in business rule customization:
- Every client has unique legacy codes, naming conventions, and override rules.
- We design override layers so your team can adjust mappings without rewriting pipelines.
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Auditability and golden layer governance:
- Full visibility into every transformation step, with lineage tracking, rollback ability, and data quality scoring.
- Ensures your stakeholders trust the harmonized layer as a single source of truth.
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What happens next?
- Our team will reach out to you to schedule a 'no pressure' call to help understand your objectives
- We'll provide relevant demos and examples. You then confirm to us that you have a formal mandate to purchase.
- We will provide the best-in-class proposition, tailored to all your nuances.
Revenue under management
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Locations globally
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Capital deployed
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