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Innovative data integration in 2024: Pioneering the future of data integration

CIO Business Intelligence

In the age of big data, where information is generated at an unprecedented rate, the ability to integrate and manage diverse data sources has become a critical business imperative. Traditional data integration methods are often cumbersome, time-consuming, and unable to keep up with the rapidly evolving data landscape.

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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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Data Integrity, the Basis for Reliable Insights

Sisense

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. What is data integrity?

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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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How IBM HR leverages IBM Watson® Knowledge Catalog to improve data quality and deliver superior talent insights

IBM Big Data Hub

Companies rely heavily on data and analytics to find and retain talent, drive engagement, improve productivity and more across enterprise talent management. However, analytics are only as good as the quality of the data, which must be error-free, trustworthy and transparent. What is data quality? million each year.

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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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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.