erwin
APRIL 2, 2021
Organizations are managing more data than ever. In fact, the global datasphere is projected to reach 175 zettabytes by 2025, according to IDC. With more companies increasingly migrating their data to the cloud to ensure availability and scalability, the risks associated with data management and protection also are growing.
Alation
MAY 19, 2022
This is data that’s artificially produced to mimic and model real events: It retains the structure of the original data but is not the same as real data. Gartner predicts that by 2030, synthetic data will completely overshadow real data in AI models. The business benefits of a data fabric are real.
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Smart Data Collective
NOVEMBER 24, 2020
billion by 2025. In this article, you’ll discover: upcoming trends in business intelligence what benefits will BI provide for businesses in 2020 and on? Business intelligence software will be more geared towards working with Big Data. Data Governance. Future of Business Intelligence: Benefits Provided.
CIO Business Intelligence
NOVEMBER 21, 2023
As chief data and AI officer for the Department of the Air Force, I was given a clear mandate to establish the department as a frontrunner in AI readiness and competitiveness. Establishing a robust data governance framework to ensure the ethical and responsible use of AI-powered technologies.
Ontotext
MARCH 8, 2023
Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. As such, data governance strategies that are leveraging knowledge graph solutions have increased data accessibility and improved data quality and observability at scale.
erwin
FEBRUARY 6, 2020
As organizations deal with managing ever more data, the need to automate data management becomes clear. Last week erwin issued its 2020 State of Data Governance and Automation (DGA) Report. One piece of the research that stuck with me is that 70% of respondents spend 10 or more hours per week on data-related activities.
IBM Big Data Hub
OCTOBER 12, 2023
The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly. But the implementation of AI is only one piece of the puzzle.
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