Remove Analytics Remove Contextual Data Remove Data Lake
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Regeneron turns to IT to accelerate drug discovery

CIO Business Intelligence

To achieve what the company would need going forward, McCowan knew Regeneron would have to undergo a major transformation and build a more enhanced data pipeline that could inject data from up to 1,000 data sources in “analytical ready formats” for both the business and the scientists to consume, the CIO says.

Data Lake 120
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The Award Winning Formula: How Cloudera Empowered OCBC With Trusted Data To Unlock Business Value from AI

Cloudera

Recently, Cloudera, alongside OCBC, were named winners in the“ Best Big Data and Analytics Infrastructure Implementation ” category at The Asian Banker’s Financial Technology Innovation Awards 2024.

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4 ways generative AI addresses manufacturing challenges

IBM Big Data Hub

Or we create a data lake, which quickly degenerates to a data swamp. Contextual data understanding Data systems often cause major problems in manufacturing firms. IBM built a workforce advisor that uses summarization and contextual data understanding with intent detection and multi-modal interaction.

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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

After countless open-source innovations ushered in the Big Data era, including the first commercial distribution of HDFS (Apache Hadoop Distributed File System), commonly referred to as Hadoop, the two companies joined forces, giving birth to an entire ecosystem of technology and tech companies. That’s today’s Cloudera.

Big Data 101
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Achieving Trusted AI in Manufacturing

Cloudera

Here are some of the key use cases: Predictive maintenance: With time series data (sensor data) coming from the equipment, historical maintenance logs, and other contextual data, you can predict how the equipment will behave and when the equipment or a component will fail. Eliminate data silos.

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Five benefits of a data catalog

IBM Big Data Hub

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. A business glossary to explain the business terms used within a data asset.