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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

Last Updated on October 31, 2024 by Editorial Team Author(s): Jonas Dieckmann Originally published on Towards AI. Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities.

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Automating Data Quality Checks with Dagster and Great Expectations

Analytics Vidhya

Introduction Ensuring data quality is paramount for businesses relying on data-driven decision-making. As data volumes grow and sources diversify, manual quality checks become increasingly impractical and error-prone.

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EU AI Act: What businesses need to know as regulations go live

AI News

Next week marks the beginning of a new era for AI regulations as the first obligations of the EU AI Act take effect. While the full compliance requirements won’t come into force until mid-2025, the initial phase of the EU AI Act begins February 2nd and includes significant prohibitions on specific AI applications.

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Narrowing the confidence gap for wider AI adoption

AI News

Business leaders still talk the talk about embracing AI, because they want the benefits McKinsey estimates that GenAI could save companies up to $2.6 In this article, we’ll examine the barriers to AI adoption, and share some measures that business leaders can take to overcome them. But now the pace is faltering.

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Meta AI’s MILS: A Game-Changer for Zero-Shot Multimodal AI

Unite.AI

For years, Artificial Intelligence (AI) has made impressive developments, but it has always had a fundamental limitation in its inability to process different types of data the way humans do. Most AI models are unimodal, meaning they specialize in just one format like text, images, video, or audio.

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Why data quality is critical for marketing in the age of GenAI

AI News

Marketers envisioning a seamless, magical customer experience must recognise that AI’s effectiveness depends on high-quality underlying data. Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality. In other words, when it comes to AI for marketing, better data = better results.

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Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.