One part, many names
Abbreviations, local terms and spelling variations split search results and create apparent duplicates.
The foundation · Automotive data readiness
Clean the data.
Make it useful.
DataDhobi cleans, standardizes and structures automotive catalogue data so it can support reliable fitment, search, analytics and AI workflows.
The data problems
Automotive catalogues need context as well as clean text.
Inconsistent part numbers
Formatting differences and supersession references complicate cross-references and matching.
Incomplete fitment
Model names alone do not resolve year, fuel, variant and emission differences.
Scope & capabilities
Make catalogue data consistent, structured and usable.
Catalogue cleaning
Detect inconsistencies, duplicate candidates and spelling variations.
Taxonomy standardization
Normalize aggregate, system, component and part classification across sources.
Description harmonization
Turn inconsistent descriptions into a common, structured form.
Category mapping
Connect products with appropriate groups, systems and families.
Attribute enrichment
Add relevant vehicle, model, fuel, fitment and usage context where supported.
Fitment readiness
Prepare records for vehicle-to-part mapping and compatibility checks.
Duplicate & error detection
Flag repeated, incomplete or conflicting records for review.
Dashboard-ready structuring
Prepare cleaned data for tools such as Power BI and further analysis.
AI/ML-ready preparation
Structure data for search, forecasting and automation workflows.
Why domain knowledge matters
DataDhobi brings automotive meaning into the process.
| Data challenge | Generic cleaning approach | DataDhobi approach |
|---|---|---|
| Synonyms & local terms | Text similarity without an automotive vocabulary | Automotive abbreviations, synonyms and local terms |
| Part numbers | Plain-string comparison | OE number formats and supersession context |
| Fitment | Limited vehicle context | Make, model, variant, fuel, emission and year context |
| Taxonomy | Source-specific categories | A consistent aggregate, system and part hierarchy |
| Validation | Blank-field and format checks | Domain logic checks alongside record-level validation |
Illustrative example
Three descriptions. One consistent structure.
Before · source descriptions
AIR FLTR ELMNT SWIFT VDIAir Filter - Swift DieselA/F ELEMENT MARUTI SWIFT DAfter · standardized fields
- Part
- Air Filter Element
- Category
- Filters › Air Filter Element
- Vehicle
- Maruti Suzuki · Swift
- Fuel
- Diesel
Illustrative description-standardization example. Part and vehicle identifiers are checked before duplicate records are merged.
From raw data to intelligence
A cleaner foundation for OEintell and your workflows.
Raw / messy data
Multiple catalogues with inconsistent, duplicate or incomplete records.
DataDhobi™
Clean, standardize, validate and structure the relevant fields.
Intelligence & analytics
A stronger data foundation for OEintell, search, dashboards and AI-ready workflows.
Continue exploring
Let's talk
Start with your business challenge.
A discovery call, a live data demo or a focused pilot. Tell us what you want to achieve.
Jinal Shah
Expandus Consulting Pvt. Ltd.
jinal@expandus.co.in+91 99606 41110Pune, India
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