An analyst’s blueprint for choosing a cloud data platform
Choosing a cloud data platform can be overwhelming with the breadth of products on the market, but following a basic strategy can not only simplify the selection process but also enable organizations to construct the right platform for their needs.
The process of deriving value from data is complex. So are many of the tools designed to help organizations realize that value. Selecting the tools that best fit an organization’s needs, therefore, is crucial.
But where to begin?
“It’s a daunting task,” Doug Henschen, an analyst at Constellation Research, said during a March 8 webinar hosted by data lake vendor ChaosSearch.
To make it less overwhelming, Henschen outlined essential steps organizations can take when choosing a cloud data platform or the capabilities to construct their own.
They begin with a self-evaluation of the organization, progress to a consideration of an overall strategy and which capabilities on the market fit that strategy, and end with a screening and testing process to ultimately choose the right set of capabilities.
Cloud data platforms are platforms where customers can not only store their data in lakes, warehouses and lakehouses but also connect analytics and data science platforms to perform analysis and data science tasks like developing augmented intelligence features and machine learning models.
Amazon Web Services, Google, Microsoft and Oracle are tech giants that offer cloud data platforms, while Databricks and Snowflake are growing vendors whose platforms enable data storage, analytics and data science. Since the onset of big data nearly two decades ago, all data management and BI platforms have aimed to simplify data exploration and analytics.
Until recently, however, they relied on connecting one tool for one task to another for another task, then still a third tool for yet another task, and so on. Data needed to be extracted, loaded and transformed over and over again to get it from one tool to another, and harnessing data for analysis was complex. Market-leading companies and fast followers are embracing things like data lakes, log analytics, data fabrics, lakehouse architectures, neural networks and so on. The most modern cloud data platforms address that complexity. They converge capabilities in a single environment — enabling data management, analytics and data science tools to work within data lakes and warehouses and other data repositories — to reduce friction. In addition, they enable automation of repetitive tasks and employ AI capabilities to further foster ease of use.
“Market-leading companies and fast followers are embracing things like data lakes, log analytics, data fabrics, lakehouse architectures, neural networks and so on,” Henschen said. But choosing the cloud data platform that best suits the needs of a particular company and effectively enables it to derive value from its data is not so simple, he added.
According to Henschen, one CEO of a major data platform vendor told a group of analysts that as much as vendors claim to offer a full set of easy-to-use capabilities, customers still struggle to put their analytics tools to good use. Likewise, Thomas Hazel, founder and CTO of ChaosSearch, noted that customers have often failed to derive value from their data platforms. ChaosSearch founder and CTO Thomas Hazel (left) discusses cloud data platforms with Constellation Research analyst Doug Henschen during a recent webinar. “There’s been a lot of promises,” he said. “The promise was to draw those insights. However, scaling business has been a real challenge. … To scale your business, you need to scale your operations. Bringing things together is a fundamental shift.
