Compare DataBuck vs. Informatica Data Quality

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

Provide immediate strategic advantages by delivering comprehensive support for the evolving demands of data quality across various users and types through AI-powered automation. Regardless of the initiative your organization is undertaking—be it data migration or advanced analytics—Informatica Data Quality offers the necessary flexibility to seamlessly implement data quality across all scenarios. Empower your business users while enhancing collaboration between IT and business leaders. Oversee the quality of both multi-cloud and on-premises data for diverse applications and workloads. Integrate human interventions into the workflow, enabling business users to review, amend, and approve exceptions during the automated process. Conduct data profiling and continuous analysis to reveal connections and more effectively identify issues. Leverage AI-driven insights to automate essential tasks and streamline data discovery, thereby boosting productivity and operational efficiency. This comprehensive approach not only enhances data quality but also fosters a culture of continuous improvement within the organization.