The constant in cloud data you can’t overlook
Cloud computing—and with it, cloud data—has seen remarkable growth over the past several years. At the close of 2018, Forrester reported that nearly 60% of North American enterprises relied on public cloud platforms, which is five times the percentage that did just five years ago. Today, the larger conversation around cloud data and cloud computing has shifted. No longer is the primary question whether or not there are benefits to cloud computing, but instead, what does successful cloud data management look like?
First, a “successful” cloud platform will look different for every organization. There isn’t one solution to cloud data management because not all data is created equal. In fact, not all data should be cloud data—hybrid solutions, or a mix of cloud and on-prem, are attractive to organizations that want to transition some workloads to the cloud, but also want to retain control over sensitive data with legacy solutions. Then there’s the question of cloud providers. The market has a clear “Big 3”—Microsoft, Amazon, and Google—but even after selecting a provider, each come with their own set of serverless offerings to best outfit organizations depending, in part, on what type of cloud data they have. And there’s roles and responsibilities to consider, as well. A platform with proper cloud data management alleviates some of the burden of a traditional IT function, such as provisioning data storage, but demands more knowledge in cloud computing. According to LinkedIn, cloud computing skills tops the list of hard skills that organizations are searching for in 2019.
“Garbage in, garbage out” holds true for cloud data
Cloud data management will look different at every organization, and the variety in cloud data perhaps even more so. But there is a constant in cloud data. No matter where cloud data is hosted or what kind of data it is, it must be cleansed, structured, and prepared for analysis. Data preparation is the constant in cloud data.
Data preparation is a constant for a reason; it is the backbone of every analytics project. Clean data, clean cloud data or otherwise, bears reliable results. Dirty data, on the other hand, costs the U.S. $3 trillion per year, Harvard Business Review estimated. But the process is often slow and tedious. Many organizations still claim that data preparation accounts for up to 80% of the overall analytic process. This is especially painful when it comes to cloud data, which holds the promise of faster accessibility. But if organizations are still using the same methods to prepare cloud data, such as hand coding with Python or R, there will always be a huge bottleneck between the time cloud data becomes available and the time it can actually be used for analytics after it has been cleansed and prepared. Finding a data preparation solution is critical for successful cloud data management among all organizations.
Alteryx Designer Cloud: The data preparation platform for cloud data
The Alteryx Designer Cloud data preparation platform offers a modern solution to the data preparation problem. Powered by machine learning, the Designer Cloud platform generates transformation suggestions with every swipe and click that the user makes. Its visual interface surfaces outliers and errors that might have otherwise slipped through to analysis. And it allows users with little-to-no coding knowledge the ability to search by name for transformation functions, while those with a deeper data science background can edit transformations directly. In short, Designer Cloud was built to tackle the data preparation problem for all types of users and for all types of data, including data in the cloud. Designer Cloud customers report up to 90% time savings after using Designer Cloud.
For those working with cloud data, Designer Cloud has made deep investments with cloud partners to ensure that it best serves these users. Designer Cloud natively integrates with major cloud platforms by leveraging the native services (including storage, compute, access controls, and security), which allows organizations to fully leverage the cost and elastic scale benefits of the cloud while also natively managing access/security in the cloud. In whichever platform your cloud data lives, Designer Cloud has extensive support. For example, Designer Cloud is an AWS certified ML Competency and Data & Analytics Competency partner. It is also the de facto data preparation service for Google Cloud Platform as Google Cloud Dataprep. And the list goes on. To learn more about the Alteryx Analytics Cloud platform and how it can help prepare your cloud data in record time, schedule a demo with our sales team.