The Primary Causes of Enterprise Data Quality Problems

Data quality problems are prevalent in organizations due to various reasons such as schema changes, API call failures, and manual data retrievals leading to duplicate data. Poor data governance can also result in expensive data silos. Machine learning algorithms are significantly impacted by the quality of data. Migrations from on-premises infrastructure to the cloud introduce new challenges related to data management and quality. The rapid growth of data volumes and sources, coupled with a plethora of data tools, create fragmented and unreliable data environments. Legacy data quality strategies fail due to their inability to scale for today's larger data volumes and ever-changing data structures. Manual ETL validation scripts are not suitable for real-time data processing and require significant ongoing engineering time and effort. Acceldata's Data Observability platform provides an end-to-end solution that helps organizations continuously optimize their data stacks, offering features like data pipeline monitoring, data reliability assessment, performance tracking, and spend visualization. Advanced AI/ML capabilities enable automatic anomaly detection and root cause identification for unexpected behavior changes in the production environment.