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Analytics in the cloud: Key challenges and how to overcome them

Like so many other IT functions, data analytics is moving to the cloud. And as with other cloud-based endeavors, this presents both opportunities and challenges.

One of the top 10 data and analytics technology trends for 2021 cited by Gartner is the use of open, containerized analytics architectures that make analytics capabilities more composable. This enables enterprises to quickly create flexible, intelligent applications that help data analysts connect insights to actions, the research firm says.

“With the center of data gravity moving to the cloud, composable data and analytics will become a more agile way to build analytics applications enabled by cloud marketplaces and low-code and no-code solutions,” Gartner notes.

The cloud can take data analytics to a new level for companies.

“Cloud enables the scalability we need for high-compute workloads,” says Aidan Taub, systems and technology director at creative services agency Loveurope and Partners (LEAP).

“As the world continues to digitize everything, organizations need to be able to build with file data at exponential scale,” Taub says. “When you have a massive amount of heavy unstructured data, like the videos, images, and audio we handle at LEAP, you never know how big the next job might be. Traditional analytics just doesn’t scale the way cloud does.”

Analytics in the cloud requires different approaches, skills, architectures, and economics compared with performing batch analysis in-house the traditional way, however. And with all this change, there are bound to be hurdles to overcome.

Here are some of the challenges organizations might face, and ways they can address them as they shift to performing data analytics in the cloud.

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