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Case Study
Data Extraction for TATA Products
Data Extraction, Product Comparison & Data Mapping for TATA
“Simplifying data structuring of fragmented records through data extraction, cleansing, and mapping, so it becomes a flawless, consistent, and analysis-ready database”
The renowned brand of India required reliable support to extract, compare, and map product information from multiple sources and directories. This case also involved handling inconsistency, accurate product matching, and fixing duplicate records across a large volume of product information. With the credible blending of smart tools like Google and Excel and human-powered verification, Eminenture introduced error-free product directories with valid and standardized records after cleansing. This approach integrated precision and consistency while getting rid of manual effort.
Challenges
Gathering product information from diverse sources needs a pair of careful eyes that checks their validity while comparing them to maintain consistency. This case involved multiple challenges, which come up next:
- Capturing product information from available sources and directories.
- Handling inconsistencies across product records.
- Matching similar products accurately.
- Identifying duplicate product information.
- Maintaining consistency across large datasets.
- Validating records against source information.
- Completing high-volume data processing within the required TAT.
Solutions Provided
Our data specialists implemented a bespoke data extraction process to capture requisite data while evaluating complementary workflow, especially to clean and process that data using human intelligence. These solutions included the following:
- Capturing & extracting product data from Google, Excel, and relevant directories.
- Comparing product information across available sources.
- Mapping products against the required data structure.
- Verifying product records for accuracy.
- Validating inconsistent information.
- Standardizing product features, attributes and formats.
- Identifying and fixing duplicate records.
- Conducting human-powered quality analysis for final verification.
- Processing and delivering 10K+ records within the agreed TAT.
Process Used
Considering the terms of its SLA, we followed a systematic workflow that made data extraction, comparison, validation, evaluation, and cleansing of product information seamless and effortless. As emphasizing accuracy and consistency was our priority, we focused on these steps:
- Source and directory identification.
- Product data capturing.
- Product comparison and matching.
- Data mapping.
- Duplicate identification and deduplication.
- Data validation.
- Product information standardization.
- Human-led data verification.
- Final data accuracy and consistency checks.
- Timely delivery of processed records.
Result
The project delivered a clean and reliable product dataset by combining structured data capturing, product comparison, data mapping, and human quality assurance:
- 10K+ product records successfully extracted.
- 99% accuracy achieved across processed data.
- Product records accurately matched and compared.
- Duplicate data identified and removed.
- Inconsistent product information validated and standardized.
- Data was put into a structured and usable format.
- Human-led QA testing strengthened overall data reliability.
- Reduced manual effort improved operational efficiency.
- Cleaner data supported reliable analysis and better insights.
- On-time TAT ensured smooth project delivery.
Presenting Performance with Facts
Figures to Measure the Accelerating Rate of Improvement
10
K+Data Extracted
0
%Accuracy
0:1
AI+Human
0
%Verified
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