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8 data strategy mistakes to avoid

CIO Business Intelligence

At Vanguard, “data and analytics enable us to fulfill on our mission to provide investors with the best chance for investment success by enabling us to glean actionable insights to drive personalized client experiences, scale advice, optimize investment and business operations, and reduce risk,” Swann says.

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Get Ready For These Six 2020 Business Intelligence Trends

Smart Data Collective

New Avenues of Data Discovery. New data-collection technologies , like internet of things (IoT) devices, are providing businesses with vast banks of minute-to-minute data unlike anything collected before. AI-Powered Big Data Technology. How Business Intelligence Will Change in 2020.

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A hybrid approach in healthcare data warehousing with Amazon Redshift

AWS Big Data

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. Additionally, scalability of the dimensional model is complex and poses a high risk of data integrity issues.

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Capgemini and IBM Ecosystem strengthen partnership for Drone-as-a-Service

IBM Big Data Hub

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The global commercial drone market is projected to grow from USD 8.15

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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

Not only does it support the successful planning and delivery of each edition of the Games, but it also helps each successive OCOG to develop its own vision, to understand how a host city and its citizens can benefit from the long-lasting impact and legacy of the Games, and to manage the opportunities and risks created.

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What is a Data Pipeline?

Jet Global

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. As data flows into the pipeline, it is processed in real-time or near-real-time.