We analyze datasets for schema issues, null patterns, anomalies, drifts, value distributions, and rule violations. Quality checks enforce constraints, consistency, referential integrity, and data type accuracy before data reaches analytics or ML models. Errors are flagged, corrected, or routed to exception pipelines with full traceability and audit logs.

We fix duplicate records, formatting inconsistencies, missing fields, typos, casing issues, mixed units, timezone conflicts, encoding errors, and semantic mismatches. Text, numeric, date, and categorical values are transformed into standardized formats, ready for downstream BI, automation, forecasting, or machine learning pipelines.

We create derived variables, time-based features, embeddings, statistical measures, frequency patterns, sentiment scores, and engineered dimensions that enhance model accuracy. Techniques include sliding windows, aggregation, lag features, domain logic, and NLP transformations — turning raw data into predictive signal instead of noise.

We unify fragmented identities across ERPs, CRMs, billing, logs, warehouse data, and customer records using fuzzy matching, clustering, vector similarity, and rules. This creates clean ‘single source of truth’ entities for customer 360, supply chain visibility, compliance reporting, and personalization engines.

We enhance internal datasets using APIs, geo-datasets, demographics, product taxonomies, financial feeds, open data, and AI-generated metadata. Enriched data improves forecasting, segmentation, fraud detection, targeting, and insights without manually collecting new inputs from teams or workflows.

We build scalable batch, micro-batch, and streaming pipelines using Airflow, dbt, Kafka, Spark, and cloud-native services. Workflows include dependency handling, retries, data validation, versioning, lineage, and monitoring — making ingestion and transformation fully automated and production-grade.