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Scaling Data Quality with Computer Vision on Spatial Data

insideBIGDATA

In this contributed article, editorial consultant Jelani Harper discusses a number of hot topics today: computer vision, data quality, and spatial data. Its utility for data quality is evinced from some high profile use cases.

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Cloud Migration Alone Won’t Solve Data Quality. Here’s Why CDOs Need a More Holistic Approach

insideBIGDATA

In this contributed article, Emmet Townsend, VP of Engineering at Inrupt, discusses how cloud migration is just one step to achieving comprehensive data quality programs, not the entire strategy.

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State of Data Quality Report

insideBIGDATA

Bigeye, the data observability company, announced the results of its 2023 State of Data Quality survey. The report sheds light on the most pervasive problems in data quality today. The report, which was researched and authored by Bigeye, consisted of answers from 100 survey respondents.

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Why Data Quality is the Secret Ingredient to AI Success

insideBIGDATA

In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes.

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What Is Entity Resolution? How It Works & Why It Matters

Entity Resolution Sometimes referred to as data matching or fuzzy matching, entity resolution, is critical for data quality, analytics, graph visualization and AI. Advanced entity resolution using AI is crucial because it efficiently and easily solves many of today’s data quality and analytics problems.

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What is Data Quality in Machine Learning?

Analytics Vidhya

However, the success of ML projects is heavily dependent on the quality of data used to train models. Poor data quality can lead to inaccurate predictions and poor model performance. Understanding the importance of data […] The post What is Data Quality in Machine Learning?

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Data Quality: The Good, The Bad, and The Ugly

KDnuggets

Incorrect or unclean data leads to false conclusions. The time you take to understand and clean the data is vital to the outcome and quality of the results. Data Quality always takes the win against complex fancy algorithms.