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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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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?

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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. and its leading data observability offerings.

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Fire Your Super-Smart Data Consultants with DataOps

DataKitchen

Analytics are prone to frequent data errors and deployment of analytics is slow and laborious. When internal resources fall short, companies outsource data engineering and analytics. There’s no shortage of consultants who will promise to manage the end-to-end lifecycle of data from integration to transformation to visualization. .

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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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The Missing Link in Enterprise Data Governance: Metadata

Octopai

We’re dealing with data day in and day out, but if isn’t accurate then it’s all for nothing!” Steve needed a robust and automated metadata management solution as part of his organization’s data governance strategy. Enterprise data governance. Metadata in data governance.