Flipboard

JUNE 17, 2025

As a result, the competitive edge is shifting toward data access and data quality. The challenge is how to use that data. Transforming unstructured files, maintaining compliance, and mitigating data quality issues all become critical hurdles when an organization moves from AI pilots to production deployments.

Data Quality 129

Data Quality AWS AI AI 129

Precisely

FEBRUARY 10, 2023

According to the IDC report, “organizations that have implemented DataOps have seen a 40% reduction in the number of data and application exceptions or errors and a 49% improvement in the ability to deliver data projects on time.” Anomalous data can occur for a variety of different reasons.

DataOps 52

DataOps Data Observability ML ML 52

Precisely

OCTOBER 16, 2023

Consequently, managers now oversee IT costs for their operations and engage directly in cloud computing contracts. This shift has influenced how cloud resources are designed and marketed, focusing on easy access, modularity, and straightforward deployment. Secure data exchange takes on much greater importance.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Machine Learning ML ML 134