Synarchy's Jayaraman on how the consulting industry is evolving and where organisations are still struggling with AI and data ...

The new initiative aims to improve EMS data quality, reporting and use, with a focus on helping rural Florida agencies ...

Objectives Maternal dietary practices are vital for improving maternal and neonatal outcomes. However, comprehensive ...

Higher education institutions are excited about the potential of artificial intelligence, but they’re often unprepared for what it demands of their data. Effective AI in higher ed isn’t just about ...

In September 2025, an assessment was undertaken of the data quality of the public sector debt statistics (PSDS) of Mauritius against the IMF’s Data Quality Assessment Framework (DQAF) for PSDS. The ...

Abstract: As diffusion models rapidly evolve, the need to incorporate manual feedback into their retraining processes has become increasingly urgent. This paper proposes a novel framework, ED3PO, to ...

Every two years since the turn of the century, a report from Oregon’s seven -member Quality Education Commission lands with a thud or a bang, depending on your perspective. The conclusion is always ...

As the push to integrate artificial intelligence and increase interoperability evolves, Clinical Architecture sees a dire need for tools that can assess the quality of healthcare data. Poor quality ...

Large law firms rode a strong 2024 on the back of broad demand and aggressive rate growth — but the model is wobbling. Expense pressure is up, realization risk is real, and AI is reshaping how clients ...

Abstract: In recent years, deep learning-based methods have made significant progress on the image quality assessment problem; however, challenges remain arising from the lack of annotated, real-world ...

Key Insight: Strict data lineage is now central to bank generative AI strategies. What's at Stake: Operational, compliance and reputational risks could translate into lawsuits and financial losses.

To establish a consistent approach to assess, manage and improve data quality across the data lifecycle, covering a wide spectrum of data types, and taking into account the blurred line between data ...