Cloudera
AUGUST 12, 2021
In my previous blog post, I shared examples of how data provides the foundation for a modern organization to understand and exceed customers’ expectations. Collecting workforce data as a tool for talent management. Collecting workforce data as a tool for talent management.
IBM Big Data Hub
FEBRUARY 5, 2024
These foundation models, built on large language models, are trained on vast amounts of unstructured and external data. They can generate responses like text and images, while simultaneously interpreting and manipulating existing data. Explore generative AI Learn more about the work IBM Consulting® is doing in generative AI.
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DataKitchen
NOVEMBER 4, 2021
Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.
IBM Big Data Hub
JANUARY 29, 2024
Part one of this series examined the dynamic forces behind data center retransformation. Now, we’ll look at designing the modern data center, exploring the role of advanced technologies, such as AI and containerization, in the quest for resiliency and sustainability. AI and containerization are not just buzzwords.
datapine
MAY 2, 2023
Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?
Ontotext
AUGUST 17, 2023
Management consultants regularly point to many of the root causes, but they often overlook a glaring common thread. These failures are at least partly due to the absence of graph technologies, at the center of those transformations, allowing companies to “connect the dots” across their data to drive optimal outcomes.
DataKitchen
JANUARY 3, 2022
Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. The data analytics function in large enterprises is generally distributed across departments and roles. Figure 1: Data analytics challenge – distributed teams must deliver value in collaboration.
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