Remove Data Integration Remove Data Quality Remove Metadata Remove Reporting
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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 Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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Alation Launches Open Data Quality Framework

Alation

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

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IBM named a leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions

IBM Big Data Hub

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. Unlock quality data with IBM. Access the full report here.

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The Missing Link in Enterprise Data Governance: Metadata

Octopai

Why aren’t the numbers in these reports matching up? We’re dealing with data day in and day out, but if isn’t accurate then it’s all for nothing!” In a panic, he went from desk to desk asking his teammates if they had been working on the same reports that day. They deal with tens if not hundreds of reports each day….

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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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SHACL-ing the Data Quality Dragon III: A Good Artisan Knows Their Tools

Ontotext

If there are any inconsistencies, the shapes will be rejected and you will receive a violation report. Data engineers think about “shapes” and “data” separately and treating shapes as data is an implementation detail. The next step is to get out there and challenge your data quality dragons.