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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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Data monetization: driving the new competitive edge in retail

CGI

The 2019 CGI Client Global Insights reveals that harnessing the power of data analytics to drive real-time insights and improved personalization for new revenue streams is a top business priority for the retail and consumer services executives interviewed. Advancing omni-channel strategies using data and emerging technologies.

Retail 96
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Q&A: Why SOAR startup Syncurity is bringing a ‘case-management’ approach to threat detection

The Last Watchdog

Enter SOAR, the acronym for “security operations, analytics and reporting.” While automation is of interest and value, the real value of the evolved SOAR market will be to have security stand-up as an enterprise process, like finance or sales and marketing. Smart money.

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How to Take Your Business to The Next Level with Data Intelligence

erwin

With tools such as Artificial Intelligence, Machine Learning, and Data Mining, businesses and organizations can collate and analyze large amounts of data reliably and more efficiently. Big IT companies even have off-the-shelf data analytics software ready to be configured by a company to their needs. Expanding big data.

Analytics 103
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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics. But today, dashboards and visualizations have become table stakes.

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How data, AI and automation can transform the enterprise

IBM Big Data Hub

Modern cloud-based data architectures support high availability, scalability and portability; intelligent workflows, analytics and real-time integration; and connection to legacy applications via standard APIs. Driven by business requirements, it establishes how data flows through the ecosystem from collection to processing to consumption.

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Data virtualization unifies data for seamless AI and analytics

IBM Big Data Hub

Data virtualization bridges this gap, allowing organizations to use their existing data sources with flexibility and efficiency for AI and analytics initiatives. This serves as a single point of reference for analytics, reporting and data-based decisions, resulting in increased accuracy and quicker generation of valuable insights.