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How cloud computing can supercharge your analytics

Discover the advantages of taking your analytics to the cloud, and learn how The Smart Cube can help you get started.

With ever more data available, organisations of all sizes are increasingly pursuing analytics projects to gain new insights, uncover new value streams, and implement new efficiencies. 

However, meaningful analytics work doesn’t come that cheap or easy. Many organisations want to gain more insights from data, but find themselves lacking the resources to do so effectively. That’s largely because the on-premises compute power needed to execute these projects are scarce. And sometimes, something as simple as provisioning the required servers can take months.

When you’re looking to gain timely insights, these delays are a real and seemingly insurmountable barrier to business value. And the costs involved can sometimes be astronomical, which places even more pressure on your projects to perform. 

If this sounds familiar, don’t be discouraged. There is another way… 

Cloud analytics: accurate, scalable insight on demand

Cloud computing has revolutionised enterprise IT over the last several years, providing scalable, on-demand resources at a low cost. Increasingly, we’re seeing more of our clients access these resources for their analytics work. 

In the context of analytics, we tend to see the cloud play two roles. The first is a Cloud-as-an-Infrastructure model, in which the core compute power and storage capacity needed to mine, source, process, and aggregate data is delivered by a cloud provider. 

For many companies, analytics processes require gigabytes, terabytes, or even petabytes of data to deliver value. The cloud has the advantage of being almost infinitely scalable, so you can easily spin up a machine and add more firepower to your data processing as and when you need to. And the fact that you can do this on demand means that you’re never overpaying for resources you don’t need.

The second approach we see is Cloud-as-a-Service, where analytics and machine learning models are deployed in the cloud and run on an ongoing basis. There are numerous advantages to this that we’ll discuss in the section below.

The four major benefits of running analytics in the cloud

The general benefits of working in the cloud are well publicised — but there are significant benefits specific to conducting analytics in the cloud, too. 

1. On-demand tools and insights 

Different cloud providers deliver a wealth of tools and solutions as part of their package, that would otherwise take a great deal of time and expertise for your engineers to code in house. 

This includes things like AWS Lambda, an event-driven, serverless computing platform that runs code in response to events, and automatically manages the computing resources required by that code.

It’s also likely that your cloud provider will capture logging information at scale, enabling you to search through that information, zero in on items of interest, and troubleshoot or improve the performance of your models.

2. Quality engineering

What’s worth bearing in mind is that these tools, along with the platforms provided by companies like Google and Amazon, are created by some of the world’s best engineers with huge cloud experience. 

And, as they’re being sold as a product to the public, there’s little room for things to go wrong without there being major repercussions. In short, you’re purchasing ready-made, tried-and-tested capabilities.

3. Security and compliance

This applies to security measures, too. When cloud technology first appeared on the scene, there were a lot of concerns around data protection. But in truth, it’s one of the safest places to store data. Cloud providers allow you to encrypt data, both during transfer to the cloud and when it’s stored in the cloud, which means you’re always compliant with security requirements. 

Similarly, with cheap and virtually unlimited storage, you can retain decades of data for audit and compliance, rather than retiring data after a certain duration due to limited on-premises capacity. 

4. Working at scale

Above all else, a cloud analytics platform opens up the doors of possibility, allowing you to scale up your analytics efforts effortlessly and affordably. This means that even the most advanced analytics projects are possible regardless of your existing IT infrastructure. 

Why isn’t everybody doing it? 

The obvious question is, if cloud-based analytics has so many benefits, why isn’t everyone doing it? 

The answer is actually quite simple — it takes a certain amount of know-how. Coding models and data pipelines to work in cloud environments is a specific skill, and if your engineers are used to working with on-premises infrastructure, it’s likely this is a skill they don’t possess. 

There can also be major compliance challenges when you begin to move data off premises, with servers hosted in different countries making it difficult to adhere to legislation without expert guidance. 

At The Smart Cube, part of our analytics offering sees us lend our expertise to organisations who want to move processes to the cloud, but lack the required knowledge and skillset. 

Our work in action

Our analytics experts have a wealth of experience with cloud analytics and have executed projects worldwide and across verticals. 

We recently worked with one of the world’s leading CPG companies, building and engineering data pipelines and helping the analytics team develop data science models for a cloud environment. 

Over the course of a few months, we helped our client build repeatable processes around data sourcing, ingestion, and validation that can be used seamlessly across different markets in Europe and America. 

These repeatable processes mean the client now has the tools to work in the cloud and scale capacity whenever the need arises. As a result, the company’s time to market has been reduced by 400%. 

If you’d like to see how we can help your organisation take advantage of the latest analytics capabilities and technologies, please get in touch.