Why Cloud Resources Are Essential For Data Scientists

For some time now, we’ve been living in the Age of Data. It isn’t hard to imagine why – never in the history of humankind there was so much information floating around. You can thank the increasing digitization of our daily lives, which has generated 44 zettabytes of data as of 2020. With such an astonishing amount of information available, it’s hardly surprising to see the growing interest in data science, the field that seeks to extract insights from that data.

Companies, organizations, institutions, and governments worldwide are now relying on data scientists to make sense of the ever-growing pile of data about customers, users, citizens, and the general public. The goal is fairly simple: to gain as much knowledge as possible to develop better strategies to engage with them. However, such a goal isn’t as simple to achieve.

Data scientists use complex algorithms, methods, and systems to gather, cleanse, organize, and analyze the available data. All of that falls under the purview of data science. This complex and interdisciplinary field is evolving rapidly thanks to several factors, of which cloud computing might stand out. How so? Because data scientists wouldn’t be where they are right now if it weren’t for the cloud resources available. Here are some of the reasons why cloud computing has become so essential for data science.

Why Cloud Resources Are Essential For Data Scientists 7

Beyond Local Limitations

Let’s imagine a data science team that only works with local infrastructure. That means that they have a local server that contains the databases with the datasets to be analyzed and the algorithms needed to cleanse, organize, and finally examine the data. So, every time this team wants to work on a new dataset, they rely on that limited infrastructure, which has some of the following drawbacks:

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    Processing speed is limited to the power of the local server.
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    The algorithm’s performance is subpar, given the limited infrastructure.
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    Near impossibility to analyze real-time data.
  • Increased reliance on manual tasks, especially when retrieving data.

This team would have their hands tied as to how far they could go. If they wanted to increase their processing power, they would need to add more servers to their local infrastructure, with everything this implies: costs, maintenance, space, tighter backup and security procedures, and so on. 

That’s why data scientists look to cloud computing for their work – to overcome these limitations imposed by local infrastructures. By moving their tools to the cloud, they can access a scalability level that no local infrastructure can offer. But there’s plenty of benefits besides that, including:

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    Cloud computing providers offer the latest server technology, which provides you with increased processing power to analyze large amounts of data in real-time.
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    Clouds provide data at all times, regardless of the location from which you want to access it.
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    Security and maintenance are the provider’s responsibility. As such, cloud infrastructures usually enjoy some of the more robust security features around.
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    Cloud providers have backup procedures in place to ensure that no data is lost.
  • Data scientists pay for what they use. Thus, they can scale up or down at any time.

Flexibility, Reliability, and Efficiency

Those are 3 of the top things any data scientist needs to get their work done in the best way possible. First and foremost, data science calls for flexible platforms to accommodate ever-changing needs. That’s especially true for data scientists that work with real-time data and need to monitor and evaluate information during peak times. 

Reliability is another major factor in why data scientists use cloud computing. Generating insights from vast data sets require a continuous and ongoing effort. Thus, teams working on analyzing data need reliable servers that can offer their peak performance at all times and without failures. Cloud computing providers aren’t foolproof, but they are usually interconnected in a way that you’ll always have a server at your disposal, even if one or several breaks down. 

Finally, there’s the efficiency of using cloud computing for data science. Having such computational power at their disposal lets data scientists move more quickly with their work. What’s more – cloud vendors have prioritized data workloads that can help them with their daily work. Thus, you can leverage IT automation or improved security governance for your data science strategy. All that combines to boost your data science efficiency and get better insights in less time.

Customizable Environment

You might argue that using the cloud for data science means that you have to fit your workflow to the offering of a third party. That’s not necessarily the case. You can hire the provider’s servers for their computational power and storage and customize it with the help of a cloud application development company. In fact, you can take a hybrid approach towards cloud computing, mixing the computational power of a third party with more neuralgic components of your private cloud. 

Thus, you can highly customize the data tools you use in the cloud to further your efficiency. For instance, this allows you to integrate AI-based solutions to automate the time-consuming process of cleansing and preparing the data before analyzing it. You can combine the latest technologies with some off-the-shelf solutions provided by the cloud computing company of your choice for everything data-related, from securing your databases to training models for better pattern recognition and, ultimately, more relevant insights.

A Challenging But Fruitful Effort

Cloud computing is a must-have for data science teams that need to scale up their efforts. However, things aren’t as easy as plugging an algorithm to a remote server and moving on. The integration between data science tools and cloud computing applications must be carefully planned to avoid potential issues and risks.

There might be problems with security and IT governance that need to be cleared out before moving forward with the integration. Your team also needs to define what processes will be kept in-house and which ones will run on the third-party servers. Finally, there are challenges regarding potential performance and scalability issues that need to be part of the data science strategy for it to be successful.

As you can see, there’s a need to find a balance between cloud computing power and the particular needs of your data science team. The first is an essential resource for data scientists, as it provides the necessary infrastructure to work as intensively as needed. But believing that there’s a one-size-fits-all solution is not the right way to go, as each company has its own particular goals that can only be achieved through customization.

Are you in the process of scaling up your data science efforts? BairesDev can help you. We can develop any cloud application you need to add to your digital infrastructure and aid you in your data science growth. Furthermore, we can integrate data science tools with any cloud environment, thus improving your flexibility, reliability, security, and scalability. Contact us now to learn more about how we can help you!

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