Data Quality Management and Governance systems have attracted the second highest investments in the space by global organizations. To maintain a single source of truth at the right time for right user, sophisticated systems rigorously profile, cleanse, segment, validate data for insight generation with zero latency. With the right mix of Man+Machine ecosystem, organizations can achieve actionable data quality in less than 6 months. Leverage our pre-built modules to accelerate your DQM maturity.Accelerate DQM maturity
DQM roadmap starts with benchmarking the data quality issues across the data lifecycle from raw sources to gold layer data models against the expected SLAs. Following up with a cost-benefit analysis, the first step is to pick the low-hanging fruits.
With latest in-memory computation technologies and machine learning approaches, detect and resolve data anomalies in real-time. Leverage Generative AI solutions to auto-correct data issues smart and easy.
Be it schematic issues in raw data, master data mismatches, KPI measurement challenges, or data integrity challenges, self-healing data networks have become widely popular in handling quality issues across the network using AI techniques.
With visual workflows in place, you can effectively monitor data pipelines in real-time, assign resolution priorities to owners, and validate AI-driven corrections in a matter of minutes.