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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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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

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This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

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Rule Output Settings within a Project in IBM Knowledge Catalog: Standardising Data Quality at Scale

IBM Data Science in Practice

However, as enterprises scale, managing data quality rules becomes increasingly complex and repetitive. Recognising this challenge, IBM has introduced a significant enhancement in IBM Knowledge Catalog (IKC) version 5.1.2 : Project-Level Settings for Data Quality Rules. Any project collaborator can view the settings.

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Data Mesh Architecture on Cloud for BI, Data Science and Process Mining

Data Science Blog

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. The integration of these technologies helps companies harness data for growth and efficiency. Each applications has its own data model.

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Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance

Dataversity

The post Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance appeared first on DATAVERSITY. They want to improve their decision making, shifting the process to be more quantitative and less based on gut and experience.

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AI in Data Governance: Enhancing Data Integrity and Security

ODSC - Open Data Science

Artificial Intelligence (AI) stands at the forefront of transforming data governance strategies, offering innovative solutions that enhance data integrity and security. In this post, let’s understand the growing role of AI in data governance, making it more dynamic, efficient, and secure.

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Advancing Data Fabric with Micro-segment Creation in IBM Knowledge Catalog

IBM Data Science in Practice

This step allows users to analyze data quality, create metadata enrichment (MDE), or define data quality rules for thesubset. Running profiling on Microsegment Explanation: Profiling ensures the microsegment aligns with analytical or governance objectives, providing actionable insights for further processing.

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