How Cloud Computing Enhances Data Science Workflows

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If data is the world’s most valuable resource, then data science is its most impactful process. As more organizations realize they need data science to retain a competitive edge, the practice becomes increasingly crucial across industries. That rapid growth is largely beneficial, but it can introduce some challenges.

Data volumes and processing demands are skyrocketing faster than conventional workflows can keep up. Data science teams need better ways to manage these rising needs, and cloud computing offers an ideal solution. Here are five reasons why.

1. Reducing Costs

Cloud computing’s cost efficiency is among its greatest strengths. Implementing and maintaining on-premise servers can be highly expensive upfront and requires significant ongoing labor and IT costs. Storing and processing data on the cloud eliminates many of those expenses.

In the cloud model, you don’t have to buy or maintain your own equipment. Considering how much processing power modern data science can require, that can represent massive savings. You also only pay for the resources you use, so any rising costs you acquire as you grow reflect actual data volume growth with no surplus.

2. Streamlining Workflows

The cloud can also streamline data science workflows. Software-as-a-service (SaaS) solutions give you access to computing speeds and capacity you may be unable to afford otherwise. Consequently, you can run more complex calculations with fewer processing delays.

Cloud systems also consolidate once-separate databases and workloads. That consolidation eliminates wasted time from switching between apps and reduces the risk of data entry and transfer errors. Bad data can significantly hinder operational efficiency, so this reliability further improves productivity.

3. Boosting Security

Despite common misgivings about cloud cybersecurity, cloud computing has several security advantages. The vast majority of cloud breaches come from human error, not technical shortcomings in the cloud itself. However, the SaaS model can make high security more accessible.

Cloud providers often have advanced security features data scientists may be unable to afford or implement in-house. That could include autonomous monitoring, automated compliance, and extensive encrypted backups. Segmenting networks is also easier in the cloud, making zero-trust and similar security architectures more accessible.

4. Expanding Data Capacity

Using the cloud also lets you store and process more data than you may be able to with an on-prem solution. Data science applications are often most effective when you have more information, but managing large data volumes on in-house systems can quickly become expensive and inefficient.

Global data volumes are on track to exceed 180 zettabytes by 2025. That can make data science more reliable than ever, but only if you have the storage capacity and computing strength to support it. The cloud makes that level of storage and analysis possible when it’d be prohibitively expensive in-house.

5. Improving Scalability

Similarly, the cloud is far more scalable than conventional data science workflows. Expanding your capacity the traditional way means buying and setting up additional servers, which is expensive and can disrupt current workflows. With the cloud, all you have to do is pay a higher rate for more capacity, and you’ll gain it immediately.

That fast scalability is critical given digital data’s current growth rate. However, if you need to downsize your operations, downscaling in the cloud is still more cost-effective than conventional means. As your capacity falls, so will your rates, ensuring downsizing doesn’t leave you with wasted hardware you’re not using.

Modern Data Science Needs Cloud Computing

Data science workflows today must be fast, reliable, secure, and able to manage considerable workloads. As those demands rise, conventional, on-premise setups quickly become insufficient.

Cloud computing offers the affordability, efficiency, security, capacity, and scalability data science teams need. Capitalizing on this opportunity will help you maximize returns from your data science applications.

 
 
April Miller is managing editor of consumer technology at ReHack Magazine. She have a track record of creating quality content that drives traffic to the publications I work with.