BIG DATA MANAGEMENT, DATA SCIENCE

6 best practices for cloud data integration

September 1, 2022

6 best practices for cloud data integration
Organizations often adopt a hybrid cloud strategy when deploying their first analytical solution to the cloud. The cloud combines sheer infinite scalability with consumption-based pricing. These are desirable attributes for analytical environments that require scalability and can be very expensive when scaled for maximum capacity over an extended period.

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OTHER WHITEPAPERS
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Adopting a Modern Data Infrastructure for Trading Analytics

whitePaper | August 25, 2022

As they seek an edge in their trading and investment activities, hedge funds and other sophisticated trading firms are looking to harness new sources of insight that can allow them to identify and exploit emerging market and industry opportunities. But many organizations are saddled with a disconnected sprawl of legacy systems that are unable to handle the demands of today’s fast and often volatile markets.

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The 2022 State of Data and What’s Next

whitePaper | February 24, 2022

Today’s business relies on access to critical data more than ever to remain ahead of the competition. Due to the challenges over the last two years, organizations must respond to greater demand for data access and a shift to supporting AI/ML in a complex, hybrid multi-cloud environment. Organizations need to become nimble in providing fast, reliable access to data anytime, anywhere, throughout their increasingly complex, integrated enterprise.

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How to accelerate operational reporting and analytics for Oracle E-Business Suite (EBS)

whitePaper | August 18, 2022

What Oracle EBS does not offer is a high-performance, quick-to-implement analytics solution that seamlessly combines data from multiple sources in an instant. Many EBS customers are stuck using multiple legacy tools and high-cost professionals to generate even the simplest reports, and are unable to modernize and migrate to more cost-effective platforms. But the demand for faster, easier, and near-instant reporting and analytics have dramatically increased over the past decade, placing tremendous pressure on CFOs, CEOs, and IT leaders.

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The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution

whitePaper | June 16, 2022

It seems like the business world has gone crazy for machine learning (ML) and artificial intelligence (AI), viewing the technologies as the essential keys to the very future of the enterprise. In fact, a recent survey from ESIThoughtLab found that two-thirds of business leaders see AI as critically important for their future. And yet, the respondents report that only 25% of AI projects are in widespread deployment and 40% of all projects are generating negative or no returns.

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The Power of Graph Data Science: Maximizing the Value Of Data Through Predictive Insights

whitePaper | September 28, 2022

Graph data science (GDS) is a scientific approach to gain knowledge from graphs, using the structures of connected data to power predictions, answer questions, and explain outcomes.

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The Evolution of Clinical Data Management into Clinical Data Science

whitePaper | September 12, 2022

Clinical Data Management (CDM) evolved over the last two decades from managing data entered on paper Case Report Forms (CRFs) to managing data transcribed into Electronic Data Capture (EDC) systems. The Society for Clinical Data Management (SCDM) strongly contributed to this first significant CDM evolution through its Good Clinical Data Management Practice 1 (GCDMP©) Chapters first published in 2000 and its certification program for Clinical Data Managers2 subsequently released in 2004.

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SmartZip Analytics Inc

SmartZip is a predictive marketing company offering a smarter way to sell products and services to the home…

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