For years, the leading enterprises across every industry have been using the cloud for application infrastructure, but now they’re aggressively heading to the cloud for analytics infrastructure as well. The benefits are already outweighing the considerations, and these businesses are finding a competitive edge by not only taking a hybrid approach to cloud-based analytics, but also going all-in with production analytics workloads in the cloud. Let’s take a look at a few reasons why.
Time to Value
It all starts with time to value. It should come as no surprise that the cloud often enables faster deployment of both analytics hardware and software. Faster deployment means faster insights into data, which gives your business a competitive edge. If a data science team needs to spin up a specific analytics environment for a just a few months while they complete a project, there is significant value in the ability to quickly deploy this environment on a few nodes in the public cloud—rather than waiting a week or more for on-premises infrastructure to be installed and configured. As analytic complexity and data volumes increase and your team needs more processing power, there is significant value in the ability to quickly scale up your infrastructure.
This leads us to the next reason cloud-based analytics will help determine tomorrow’s winners: flexibility.
A recent study by BARC Research1 showed that flexibility is the top reason enterprises are adopting the cloud for analytics. These findings are consistent with the majority of surveys around IT leaders: flexibility is key. Cost, as well as reduced hardware/software maintenance, trailed closely behind. The reality is that cost considerations are closely related to flexibility for obvious reasons—the ability to scale and flex your infrastructure means you can pay only for what you use. For example, you could get discounted cloud analytics infrastructure on a three-year commit, but scale up your processing power temporarily with hourly on-demand pricing during peak times for processing analytics workloads.
IT leaders are typically given a budget, and the more analytics infrastructure/capabilities they can squeeze into that budget, the more wins they can enable for their business.
Data gravity is another factor for why enterprises are embracing the cloud. If your business is already leveraging the cloud for application infrastructure and subsequently storing app-based data in the cloud, then it is often not only cost-effective, but also shortens development cycles, when moving your analytics to where this data resides. Public cloud scalable storage solutions, such as AWS S3 and Azure Blob storage, are cheap and reliable. Some cloud-based analytics solutions, such as Teradata, integrate directly with these storage mediums for backup and allow you to run queries directly from your analytics environment that pull in this data ad hoc. This means your data analytics/scientists have access to a wider variety of data from a single analytics environment in the cloud, enabling them to provide more effective business insights at a lower cost.
Access to Innovation
Faster access to innovation can be the determining factor for tomorrow’s winners. Your business relies on a wide variety of analytics tools and technologies, and companies can often build them at a more rapid pace in the cloud. Why? Because they can test new features and capabilities at scale without waiting on the installation of on-premises infrastructure. This allows for a more rapid pace of innovation in which new capabilities first become available on cloud-specific deployments. Plus, your business can often perform an upgrade—hardware or software—more rapidly in the cloud and take advantage of these new capabilities. This approach includes not only improved methods for analyzing data, but also features quicker deployment, greater flexibility, and tighter integration with other cloud services (think data gravity). And the virtuous cycle of benefits repeats itself.
Teradata in the Cloud
Teradata is leading the analytics-in-the-cloud space with flexibility of our own. Not only do we provide the flexibility for our customers to choose where they can quickly deploy a Teradata environment—AWS, Azure, and our own infrastructure—we also offer new capabilities that make it quick and easy to scale, as needed. With Teradata in the public cloud, you can scale compute and storage independently in minutes, and you can likewise stop and re-start the database to save on compute costs during off-periods.
A wide variety of enterprises are leveraging Teradata in the cloud to run their full production workloads at scale—and they’re winning. A quick search for “Teradata” on the AWS or Azure Marketplaces will get you started on deploying Teradata in the cloud, so you too can see how quickly your business can win with cloud-based analytics.
To learn more about Teradata in the cloud, I encourage you to follow the conversation at #CloudExperts or #BuiltForTheCloud, or reach out to your Teradata account executive.
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As Director of Teradata's Cloud Solutions, Scott Dykstra is responsible for a team of architects that design and deploy analytic environments in the Cloud for Teradata's customers across the Americas. His team also defines best practices for Cloud analytics and provides direction for product development from a field perspective. Scott is a results-oriented technology executive that led Teradata's transformation in the Cloud, and continues to set strategy and market positioning today.
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