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Success in analytics requires having data you can trust.
Analytics requires structured data, but we may be introducing errors in the processing we use to produce it.
In addition, Big Data analytics requires new and sophisticated algorithms based on machine and deep learning techniques to process data in real-time with high accuracy and efficiency.
The growing gap between users and the big data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity.
High-achieving executives have been shaped by the pivotal experiences of their careers, yet analytics requires executives to think beyond these mental models.
More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators.
Similar(40)
So, too, operational analytics require forgoing some analytical power and customization in order to create analytics processes that can increase results in situations where a fully custom predictive model just doesn't make sense.
Technology and analytics require new skills.
These applications include wearable and body sensors, advanced pervasive healthcare systems, and the Big Data analytics required to inform these devices.
The size and complexity of the molecular dynamics simulations and the analytics required the use of DOD HPCMP High Performance Computing HPCC) resources.
The databases are expensive, the algorithms are proprietary, the results of experiments by campaigns are secret, and the analytics require special expertise.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com