Precisely

FEBRUARY 20, 2025

Follow five essential steps for success in making your data AI ready with data integration. Define clear goals, assess your data landscape, choose the right tools, ensure data quality and governance, and continuously optimize your integration processes. Thats where data integration comes in.

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Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.

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IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Precisely

JUNE 26, 2023

Those who have already made progress toward that end have used advanced analytics tools that work outside of their application-based data silos. Successful organizations also developed intentional strategies for improving and maintaining data quality at scale using automated tools. The biggest surprise?

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Alation

JULY 6, 2021

Auto-tracked metrics guide governance efforts, based on insights around data quality and profiling. This empowers leaders to see and refine human processes around data. Deeper knowledge of how data is used powers deeper understanding of the data itself. Siloed Data. Why Do Data Silos Happen?

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phData

SEPTEMBER 1, 2023

By combining data from disparate systems, HCLS companies can perform better data analysis and make more informed decisions. See how phData created a solution for ingesting and interpreting HL7 data 4. Data Quality Inaccurate data can have negative impacts on patient interactions or loss of productivity for the business.

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The MLOps Blog

MAY 17, 2023

These pipelines assist data scientists in saving time and effort by ensuring that the data is clean, properly formatted, and ready for use in machine learning tasks. Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

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