Trifacta Unveils New Set of Features for Efficient Machine Learning

Trifacta, a provider of data preparation solutions, is releasing a new set of capabilities specifically focused on making data quality assessment, remediation, and monitoring more intelligent and efficient.

The new capabilities are designed to help organizations modernize their approach to addressing data quality issues that hinder the success of analytics, machine learning, and cloud data management initiatives.

The first new capability, Active Profiling, is a selection model that blends real time visual and interactive guidance with machine learning, helping users discover and interact with data quality issues and resolve them with intelligent suggestions — all while sharing live previews to ensure that user validation is built into every step.

Second, Smart Cleaning is a set of new features to address data quality issues that arise in formatting and standardization. With Cluster Clean, Pattern Clean, and Reference Clean, users can choose from a variety of different intelligent cleaning approaches to resolve data quality issues with mismatched data formatting and miscategorizations.

“To improve the speed, scale, and accuracy of data quality processes, they must transition from being completely manual, siloed activities, to collaborative initiatives that are automated by machine learning and driven by the users who know the data the best,” said Wei Zheng, vice president of products at Trifacta. “Trifacta’s expansion into Data Quality with the introduction of Active Profiling and Smart Cleaning will help organizations democratize data quality remediation while maintaining governance. As a result, the efficiency and value of their analytics initiatives will significantly improve.”

Later in 2019, Trifacta will focus on bringing data quality to the automation process with the rollout of additional functionality to support flow orchestration, monitoring, and alerting.

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