20,000 batch inspections. Duplicates, nulls, and outliers baked in.
Instant profiling, quality flags, visualizations, and a plain-English synopsis — automatically. Then ask questions or generate SQL.
About the data
Download CSV20k batch inspection records with intentional DQ issues: ~3% duplicate batch IDs, null defect_type values, outlier defect_counts, and non-standard qc_result entries. Designed for data profiling and DQ rule validation.
Data is synthetic and for demonstration purposes only. Inspired by UCI ML Repository · Steel Plates Faults
Explore Other Datasets
Retail Daily Sales
25k daily POS transactions across 20 stores, 5 regions, and 8 product categories over two years. Rich time-series structure for trend decomposition, regional comparisons, and seasonal pattern analysis.
E-Commerce Orders
50k online orders over two years with product categories, shipping methods, return flags, and order status. Includes intentional dirty data: inconsistent category casing and ~5% null shipping_method values.
Manufacturing OEE
30k hourly shift records across 20 machines over 500 days. Availability, performance, quality sub-scores, and defect counts at the machine-shift level — ideal for pivot/aggregation to machine-level, line-level, or daily rollups.
Supply Chain Shipments
30k shipment records with order date, dispatch, expected and actual delivery, carrier, weight, freight cost, and on-time flag across 10 origin/destination city pairs. Suited for SLA monitoring and pivot by carrier × region.
Inventory Levels
15k daily SKU-level stock snapshots across 300 SKUs and 5 warehouses. Includes stock value and a below-reorder flag — wide structure designed for unpivoting, reshaping to trend series, and warehouse-level aggregation.
Healthcare Admissions
25k hospital admission records with three deliberate DQ issues: mixed date formats (ISO/US/UK), ~8% null age_group, and ~2% duplicate admission IDs. A realistic scenario for date standardisation and null-imputation workflows.
Frequently Asked Questions
What is the Product Quality Control dataset?▾
20k batch inspection records with intentional DQ issues: ~3% duplicate batch IDs, null defect_type values, outlier defect_counts, and non-standard qc_result entries. Designed for data profiling and DQ rule validation.
How many rows and columns does the Product Quality Control dataset have?▾
The Product Quality Control dataset contains 20,000 rows and 10 columns. The file size is approximately 1.3 MB in CSV format.
What can I do with the Product Quality Control dataset in Infoveave?▾
You can upload the Product Quality Control CSV directly into Infoveave and use Fovea AI to get instant profiling, data quality checks, visualizations, and a plain-English synopsis. You can also ask natural-language questions or generate SQL — no coding required.
Is the Product Quality Control dataset free to download?▾
Yes, the Product Quality Control dataset is completely free to download as a CSV file. It is a synthetic dataset inspired by publicly available manufacturing data patterns, created for demonstration and learning purposes.
What use cases is the Product Quality Control dataset suited for?▾
The Product Quality Control dataset is suited for data quality. Common applications include dirty data, duplicates, null handling.
Ready to see Infoveave in action?
