Dataversity

JUNE 29, 2022

You want to rely on data integrity to ensure you avoid simple mistakes because of poor sourcing or data that may not be correctly organized and verified. The post Data Observability and Its Impact on the Data Operations Lifecycle appeared first on DATAVERSITY. That requires the […].

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SEPTEMBER 18, 2023

Author’s note: this article about data observability and its role in building trusted data has been adapted from an article originally published in Enterprise Management 360. Is your data ready to use? That’s what makes this a critical element of a robust data integrity strategy. What is Data Observability?

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DECEMBER 12, 2023

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your data observability strategy. Learn more here.

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IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Organizations must support quality enhancement across structured, semistructured and unstructured data alike.

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FEBRUARY 10, 2023

Read Here are the top data trends our experts see for 2023 and beyond. DataOps Delivers Continuous Improvement and Value In IDC’s spotlight report, Improving Data Integrity and Trust through Transparency and Enrichment , Research Director Stewart Bond highlights the advent of DataOps as a distinct discipline.

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FEBRUARY 9, 2024

For instance, a data breach or violation of privacy standards can lead to liability, expensive fines, and a slew of negative publicity that’s a hit to brand reputation and trustworthiness. Going into 2024, we see a continued trend towards enabling democratized delivery of data across business functions with repeatable, scalable processes.

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JULY 15, 2024

Multiple domains often need to share data assets. Quality and formatting may differ with more autonomous domain teams producing data assets, making interoperability difficult and data quality guarantees elusive. Data discoverability and reusability.

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