Business Challenge
A leading North American B2B distributor with a large and diverse product catalog faced significant data quality challenges. Decades of growth, acquisitions, and supplier feeds had resulted in:
Duplicate item records with inconsistent SKUs, descriptions, and attributes
Redundant customer accounts causing fragmented sales and service visibility
Overlapping supplier entries complicating procurement and payment processes
These data issues led to operational inefficiencies, reporting inaccuracies, inventory mismatches, and reduced trust in analytics and downstream systems.
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The Solution
Iksula deployed an AI/ML-powered deduplication engine to cleanse and unify product, customer, and supplier data across multiple systems. Our solution combined machine learning models, fuzzy matching algorithms, and NLP techniques to detect, cluster, and merge duplicate records with high precision.
Key Capabilities:
Item Data Deduplication: Identification of duplicates using product names, attributes, descriptions, and cross-supplier references — even with partial or unstructured data.
Customer Data Unification: Detection of duplicate accounts using similarity scoring on names, addresses and contact info.
Supplier Master Cleanup: Consolidation of vendor records by matching legal entities, tax IDs, and payment details.
Continuous Monitoring: Machine learning models continuously refine matching logic as new data enters the system.
Business Impact
Data Accuracy Improved by 98%: Unified item, customer, and supplier records across platforms.
Operational Efficiency: Reduced manual data cleanup time by 70%.
Better Insights: More accurate analytics and reporting, enabling confident business decisions
Cost Reduction: Lower procurement errors, fewer duplicate payments, and improved pricing accuracy
Technology Stack
ML Algorithms: Fuzzy string matching, TF-IDF, cosine similarity, clustering models
NLP: Attribute extraction and semantic matching from unstructured descriptions
