Precisely

JANUARY 9, 2025

Artificial Intelligence (AI) is all the rage, and rightly so. But those end users werent always clear on which data they should use for which reports, as the data definitions were often unclear or conflicting. Business glossaries and early best practices for data governance and stewardship began to emerge.

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Precisely

SEPTEMBER 19, 2024

Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications. Using AI systems to analyze and improve data quality both benefits and contributes to the generation of high-quality data.

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IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. A high overall score indicates that a dataset is reliable, easily accessible, and relevant.

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Pickl AI

OCTOBER 18, 2023

However, the mere accumulation of data is not enough; ensuring data quality is paramount. The Significance of Data Quality Before we dive into the realm of AI and ML, it’s crucial to understand why data quality holds such immense importance. This enhances the depth and usefulness of the data.

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Precisely

JUNE 26, 2023

They shore up privacy and security, embrace distributed workforce management, and innovate around artificial intelligence and machine learning-based automation. The key to success within all of these initiatives is high-integrity data. The biggest surprise?

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Precisely

SEPTEMBER 11, 2023

Artificial intelligence (AI) has many applications, ranging from software products to appliances to cars and everything in between. By bringing the power of AI and machine learning (ML) to the Precisely Data Integrity Suite, we aim to speed up tasks, streamline workflows, and facilitate real-time decision-making.

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Snorkel AI

MAY 12, 2023

The magic behind these experiences is most often attributed to artificial intelligence and machine learning. That’s data. Our data teams focus on three important processes. For example, where verified data is present, the latencies are quantified. Data governance and monitoring.

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