How the Cloud Finally Became the Standard for Data Science Projects

Machine learning and other complex algorithms are becoming standard across various disciplines, including business intelligence, marketing, customer support, social media and fraud detection. And, a lot of the time, they’re used to build cloud-based prediction models with a high level of accuracy.

Companies all over the world are constantly collecting massive amounts of data, encompassing everything from payment transactions to website clicks and factory output. Sooner or later it becomes difficult for most of these companies to extract meaningful insights from this data. Data storage and data processing solutions have come a long way since the days of centralised computer systems and departmental servers in organisations.

For example, many businesses need a way to match their current data sets against historical patterns for predictive analysis. This helps executives make informed business decisions when it comes to preventing churn in their customer base, identifying customers with the highest lifetime value, pinpointing next best action, or maximising revenue opportunities through up-selling and cross-selling.

The technologies needed to make this happen are designed to identify patterns, analyse trends, and make predictions based on the collected data. And it’s the cloud which provides the processing, computing resources, and big data support that’s needed to undertake these projects.

Since all of this data has to be collected, analysed, and interpreted in large volumes and as quickly as possible, it’s essential that these processes take place in the cloud. The process of consolidating data and transforming it into meaningful insights is resource intensive and requires powerful tools. As an example, Sisense for Cloud Data Teams empowers businesses to connect to cloud data sources, build data pipelines, and perform advanced analysis.

Before we discuss how the cloud finally became the standard for data science projects, let’s take a step back to evaluate the reality of working in a world without the cloud.

Remembering a World Without the Cloud

To fully understand the benefits of the cloud in the context of data science, let’s examine the reality of working with databases that run locally.

Data scientists used to transfer data to their personal workstation machines from the company’s central database whenever they needed to run an analysis or work on a project. Only once the data was local, they would be able to work with it. This means that they’d essentially manually retrieve slices of data sets for each required query. The drawbacks of this sort of approach were three-fold: low processing speeds, a single point of failure, and limited computing resources.

More specifically, when working with data locally, the processing speed you had access to was entirely dependent on your machine’s computing power. In addition, you would only be able to work with a limited amount of data at a time, due to the limited computing resources your machine provided.

Since working with databases locally was hardly a viable option for the long term, data scientists turned to the next best thing: on-prem servers. However, they came with their own set of drawbacks and limitations. On-prem servers need large, climate-controlled, secure physical space where they’re stored and managed. It’s also important to bear in mind that server infrastructure is expensive to set up and maintain. Plus, you still have to create backups, which requires even more servers.

So, to sum it up, a world without the cloud is either slow in terms of processing and limited considering the computing resources available or incredibly expensive to set up and maintain.

How the Cloud Became Essential

Data science and analytics projects require relatively large amounts of processing power. For many years, using software for building predictive analysis models was unthinkable for most businesses mainly due to the high costs involved. And even if a company could afford to implement it, they didn’t have anyone with the technical knowledge for designing a predictive model or gleaning meaningful insight from it.

However, the cloud has completely changed the landscape. Today, it’s more cost-effective to implement and operate cloud-based solutions than on-prem servers. Businesses don’t have to manage cloud servers themselves, which means they don’t have to worry about where to store the physical server.

In terms of setup, cloud servers are ready to go and don’t require any installations or formatting. Plus, the data stored in a cloud instance is usually backed up on different server farms, which means you don’t have to buy another server to ensure multi-location redundancy.

Probably the biggest advantage cloud-based solutions offer, though, is that they give businesses access to scalable, high-performance infrastructure at a fraction of the cost. And over the years, cloud services have evolved to include solutions built from the ground up for data warehousing purposes and analytics SaaS for connecting and consolidating data sources. These solutions require a lot of processing power and computing resources, so it’s more cost-effective for companies to rent the infrastructure (cloud) than to purchase it (on-prem server).

New providers have emerged in recent years with SaaS offerings built from the ground up specifically for collaboration in the cloud – often with cloud warehousing capabilities baked into the software’s user experience. And as a result, the cloud went from something that was great-to-have to the lifeblood of modern organisations.

In addition to processing power and computing resources, the cloud brings other benefits to the table that are worth mentioning. For example, the cloud offers businesses access to affordable data storage. This is particularly important considering the vast amount of data companies collect every day.

As the data which companies collect continues to grow in volume, it becomes more and more expensive for them to house in their own data centres. The cloud, however, is much more cost-effective.

The cloud also makes it possible for businesses to scale up as their projects progress. In other words, it enables businesses to pay only for what they use. It essentially empowers more people in an organisation to experiment with machine learning options that don’t require them to be exceptionally knowledgeable about machine learning and artificial intelligence algorithms. In this way, the cloud makes collaboration between teams possible while increasing efficiency and productivity.

Smaller businesses have benefited from widespread cloud adoption in data science the most as it gives them relatively low-cost access to the same tools and technologies as enterprise organisations.

To wrap your head around this point, consider that companies with major investments in equipment and infrastructure (like those in the manufacturing or logistics and transportation industries, for example) need to be able to contain costs. They can analyse the data and metrics related to product life-cycle maintenance of the equipment they use to accurately predict timelines for probable maintenance events and their corresponding expenditure. As a result, they can effectively prevent critical downtime and stay operational, so you could say that, the cloud has democratised data analysis and data science.

Conclusion

Historically, companies have used centralised computers and departmental servers in their data centres. However, the proliferation of data in recent years necessitates the implementation of cloud data storage and processing solutions.

The easy-to-implement and easy-to-use infrastructure that the cloud provides is incredibly attractive – especially for smaller companies – and applies directly to the different applications data scientists use in their projects.

Considering that on top of the relatively cost-effective infrastructure, better processing power, and additional computing resources, most cloud providers make it easy for users to access open-source frameworks, it’s easy to see how the cloud became the standard for data science projects. It enables businesses to collect, analyse, and interpret massive volumes of data and make informed business decisions.