Remove Data Architecture Remove Data Integration Remove Data Quality Remove Data Warehouse
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Data architecture strategy for data quality

IBM Big Data Hub

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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How Knowledge Graphs Power Data Mesh and Data Fabric

Ontotext

Bad data tax is rampant in most organizations. Currently, every organization is blindly chasing the GenAI race, often forgetting that data quality and semantics is one of the fundamentals to achieving AI success. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

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How Metadata Makes Data Meaningful

erwin

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.

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Cloud Data Warehouse Migration 101: Expert Tips

Alation

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

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Data Strategies for Getting Greater Business Value from Distributed Data

Data Virtualization

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

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How Metadata Makes Data Meaningful

erwin

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.

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How to Pinpoint Where Your Organization Wins (and Loses) with Data

CIO Business Intelligence

Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. Layering technology on the overall data architecture introduces more complexity. For data warehouses, it can be a wide column analytical table.