Search Results: ai data quality (585)

Aimed at business decision-makers, this guide covers the steps needed to put an effective data governance policy in place.

There are some practical steps that organizations can take to implement important data governance, says Frank Domizio.

Through the effective use of social determinants of health (SDOH) data, the healthcare industry can work toward improved health outcomes and greater health equity.

By using AI frameworks, companies can ensure transparency, compliance, and more, which will lead to building customer trust in AI systems.

Universal access to healthcare information – including patient records – is still one of the healthcare industry’s biggest pain points. There are important advances taking place, however.

With artificial intelligence heading toward mainstream status, it’s time for CFOs to develop a game plan for implementing this powerful technology in their organizations.

With the AI-driven Intelligent Data Management Cloud, Informatica is helping customers optimize data management and drive digital transformation.

Excel spreadsheets have limitations leading to data quality and integrity issues and should be replaced by modern cloud tools, asserts Wayne Sadin.

To build and maintain customer customer trust, businesses must understand how to identify and eliminate bias within their AI models.

To prevent data breaches, organizations must prioritize data governance; there’s a big collaboration opportunity between the CISO and CDO roles in this endeavor.

With the advancement of natural language processing (NLP) tools, guardrails and content moderation are critical to combat bias that can creep in.

The release of the “DC-Check” framework by researchers at UCLA and the University of Cambridge has called attention to the need for standardized AI frameworks.

AI and automation can protect against financial and reputational losses from data theft and ransomware in financial software, writes Bill Doerrfeld.

Data security strategy must go beyond securing a company’s data and employees to protect every data channel and counterparty. Wayne Sadin presents guidelines for managing third-party data risks.

By positioning artificial intelligence (AI) as an “everyday,” democratized technology, Dataiku is fueling expanded use cases and digital transformation initiatives.

Wayne Sadin shares why the advent of generative AI puts pressure on organizations to tighten up their data and security strategies to avoid bias and protect sensitive data.

Toni Witt looks at how a healthcare application of Automation Anywhere’s hyperautomation reflects the need for human input to be successful.

Chief Data Officers self-report massive sprawl in their data sources, highlighting the importance of centralization and governance.

AI accuracy depends on the quality of the data it is fed. Toni Witt explores ways that datasets can be cleaned, or even generated, to make AI better.

Healthcare expert Paul Swider analyzes the push for sustainability in healthcare how industry participants are developing technology and business approaches to reduce waste.