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Mastering healthcare data governance with data lineage

IBM Journey to AI blog

The healthcare industry faces arguably the highest stakes when it comes to data governance. For starters, healthcare organizations constantly encounter vast (and ever-increasing) amounts of highly regulated personal data. healthcare, managing the accuracy, quality and integrity of data is the focus of data governance.

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How to Assess Data Quality Readiness for Modern Data Pipelines

Dataversity

The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess Data Quality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.

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Data Fabric and Address Verification Interface

IBM Data Science in Practice

How can organizations get a holistic view of data when it’s distributed across data silos? Implementing a data fabric architecture is the answer. What is a data fabric? Ensuring high-quality data A crucial aspect of downstream consumption is data quality.

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Using Agile Data Stacks To Enable Flexible Decision Making In Uncertain Economic Times

Precisely

This requires access to data from across business systems when they need it. Data silos and slow batch delivery of data will not do. Stale data and inconsistencies can distort the perception of what is really happening in the business leading to uncertainty and delay.

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Alation + Soda: Dynamic Data Quality with the Data Catalog

Alation

Do we have end-to-end data pipeline control? What can we learn about our data quality issues? How can we improve and deliver trusted data to the organization? One major obstacle presented to data quality is data silos , as they obstruct transparency and make collaboration tough. Unified Teams.

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Data architecture strategy for data quality

IBM Journey to AI blog

What does a modern data architecture do for your business? A modern data architecture like Data Mesh and Data Fabric aims to easily connect new data sources and accelerate development of use case specific data pipelines across on-premises, hybrid and multicloud environments.

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Why Is Data Quality Still So Hard to Achieve?

Dataversity

We exist in a diversified era of data tools up and down the stack – from storage to algorithm testing to stunning business insights.