Remove Data Quality Remove Data Silos Remove SQL
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Enhancing Data Fabric with SQL Asset Type in IBM Knowledge Catalog

IBM Data Science in Practice

In this blog, we explore how the introduction of SQL Asset Type enhances the metadata enrichment process within the IBM Knowledge Catalog , enhancing data governance and consumption. It enables organizations to seamlessly access and utilize data assets irrespective of their location or format.

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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning Blog

By analyzing their data, organizations can identify patterns in sales cycles, optimize inventory management, or help tailor products or services to meet customer needs more effectively. This tool democratizes data access across the organization, enabling even nontechnical users to gain valuable insights.

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Data architecture strategy for data quality

IBM Journey to AI blog

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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The power of remote engine execution for ETL/ELT data pipelines

IBM Journey to AI blog

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. This situation will exacerbate data silos, increase costs and complicate the governance of AI and data workloads.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata. Effective data quality management is crucial to mitigating these risks.

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Learn the Differences Between ETL and ELT

Pickl AI

This phase is crucial for enhancing data quality and preparing it for analysis. Transformation involves various activities that help convert raw data into a format suitable for reporting and analytics. Normalisation: Standardising data formats and structures, ensuring consistency across various data sources.

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Why The Public Sector Needs Data Governance

Alation

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.