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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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The Enterprise AI Revolution Starts with BI

Jet Global

Many of the features frequently attributed to AI in business, such as automation, analytics, and data modeling aren’t actually features of AI at all. Which problems do disparate data points speak to? Enter data warehousing.

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Five benefits of a data catalog

IBM Big Data Hub

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

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The Future of AI in the Enterprise

Jet Global

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing. To truly benefit from artificial intelligence, you need to set the stage with effective reporting and analytics.

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The Future of AI in the Enterprise

Jet Global

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing. To truly benefit from artificial intelligence, you need to set the stage with effective reporting and analytics.

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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.

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How to Design an Analytics Stack that Humans Actually Use

Alation

percent) cite culture – a mix of people, process, organization, and change management – as the primary barrier to forging a data-driven culture, it is worth examining data democratization efforts within your organization and the business user’s experience throughout the data analytics stack.