Data Operations & Quality

• Label structured/unstructured data for ML models (images, text, audio)
• Annotate image datasets for computer vision training
• Tag text data for NLP and sentiment analysis
• Classify audio files for speech recognition models
• Create training datasets with quality-controlled labels

• Reformat and QA legacy data for analytics or software migration
• Migrate data from legacy systems to modern platforms
• Normalize data formats across multiple sources
• Transform data schemas for new system requirements
• Validate data integrity during migration processes

• Continuous QA on dashboards and BI pipelines
• Set up automated data quality monitoring
• Validate data accuracy in real-time systems
• Monitor data pipeline health and performance
• Create data quality dashboards and alerts

• Clean, deduplicate, and enrich CRM or product data across systems
• Standardize contact information and remove duplicates
• Enrich customer profiles with third-party data sources
• Validate email addresses and phone numbers
• Normalize address and location data

• Categorize product or content data for search, UX, or SEO
• Tag products with standardized categories and attributes
• Organize content libraries with metadata tags
• Enhance search functionality with structured tagging
• Optimize SEO through strategic content categorization

What This Looks Like in a Budget Line

• Data QA
• Operational Support
• Vendor Data Cleaning
• Analytics Readiness

With context-aware AI and human-in-the-loop execution, Work Simplr turns hidden know-how into 28-day sprints that deliver real outcomes.