What makes a data strategy prosperous? Perhaps its employee success, or customer satisfaction. Maybe it’s a measure of revenue. As new benchmarks emerge, enterprises find that automation is crucial to dealing with data at the edge, in real-time. Executing successfully remains a challenge.

“We are in a race to find new ways to use data to move the business forward, satisfy your customers, and so forth,” said Bryan Kirschner (pictured), vice president of strategy at DataStax Inc. “What’s not okay is to lose track of where you need to be relative to how the market’s moving.”

It’s no longer enough to actionize business data. According to Kirschner, it’s become a question of converting insights into actions at the right place and at the right time, if there’s any hope to survive the current pace of change.

Surveying hundreds of IT practitioners, DataStax recently found hybrid cloud a common thread amongst the most successful organizations driving revenue through data, as containers enable data and compute power to meet up when and where needed. To manage hybrid at scale requires automation, an industry-agnostic advancement now blossoming in the fertile fields of open source computing.

Joining theCUBE, SiliconANGLE Media’s livestreaming studio, Kirschner spoke with host Lisa Martin to discuss DataStax’s efforts to benchmark successful data strategies, digging into the company’s latest industry survey, titled “The State of the Data Race 2021.” (* Disclosure below.)

From data science to data strategy

DataStax surveyed more than 500 executives and technical practitioners, across a range of industries and company sizes, about their data strategies. Nearly all (96%) report that their organization indeed has a data strategy, yet just 38% give a top-box score on using data to create value for customers. And only 17% say data and analytics drive more than 20% of revenue.

What can be learned from the minority able to attribute more than 20% of revenue to data analytics? According to DataStax’s survey findings, successful data strategies are twice as likely to use a cohesive open source stack. Data leaders were also four times more likely to have deployed Apache Cassandra, Kubernetes, and two of any of the following open source technologies: Apache Spark, Apache Pulsar, Apache Kafka, or Elasticsearch, a combination DataStax has dubbed the “open data stack.”

“The track record of open source is strong. You look at the cycle of innovation and see Pulsar having emerged as a sort of newer, more cloud-friendly version of Kafka … that cycle of innovation arguably is accelerating,” said Kirschner. 

Such open source tools are considered the “best of the internet” according to the DataStax report, having proven themselves by solving some of the biggest data problems to date. What DataStax is seeing is a maturity across open source implementations to allow businesses to allocate resources for innovation rather than reinventing the open source wheel. Automation is the next step in abstracting away the complexities of open source.

It won’t be computing infrastructure that sets companies apart, but rather the application of domain knowledge towards customer experiences, according to Kirschner.

“As you think about what’s unique to you as a company, it’s the data you have. No one has the customer interactions you have. What you want to do is take those best-of-breed tools and have flexibility about the infrastructure services to support them, and focus your people on doing great things with the data,” said Kirschner.

“When you see the leaders leaning hard into open source, you know it’s because they’ve got clarity about using these best-degree tools on [their] data,” he said.

Watch Kirschner’s complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations. (* Disclosure: DataStax Inc. sponsored this segment of theCUBE. Neither DataStax nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE