As mainframe systems are becoming outdated, Wayne Sadin and Adam Meyer discuss how Qlik provides analytics and data migration solutions for modernization.
Search Results: ai data quality (529)
With DroidGPT, Endor Labs applies generative AI to software development to ensure open-source packages are current and secure.
From education to the creator economy, few sectors are immune to the disruptive effects of generative AI, but different generations look at (and must approach) the technology differently.
By integrating generative AI with automation, industries can provide personalized customer experiences and meet shifting market conditions.
Wayne Sadin explains organizations can make the most of their big investments in generating and acquiring data with the help of SaaS data lakehouses.
Microsoft’s updates to its Azure AI Services for Health and Healthcare Cloud, set to be previewed at the HIMSS conference this week, signal a transformative leap forward.
Data quality impacts decision-making, efficiency, productivity, and customer experience; all business leaders must assign it a high priority.
End users with little to no IT background can learn data engineering and data science with the help of low-code/no-code platforms, but they must understand foundational aspects of the fields to avoid creating messy situations.
As a business leader today, your success relies on accurate data, real-time analytics, and actionable intelligence. Here are the tools to help you.
ESG data can be used to support sustainability in healthcare, but it’s not without its challenges and opportunities for C-suite leaders.
By leveraging environmental, social, and governance data, healthcare providers can optimize resource use, minimize waste generation, and enhance patient outcomes.
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.