IBM beefed up its cloud platform recently with dozens of new data services that extend popular open-source technologies to enterprise customers.

The rallying cry from IBM these days is "open for data" -- a commitment to proven, open technologies and standards that "elevate data to a first-class citizen" according to Adam Kocolowski, CTO of IBM Analytics Platform and Cloud Data Services.

That vision manifested itself in the new offerings Big Blue added to its Bluemix Platform-as-a-Service -- services that enable users of all levels to get working with large data sets and advanced analytic methods, he said.

Kevin Goodman, managing director at BlueBridge Networks, a dedicated and cloud hosting provider based in Cleveland that offers the IBM cloud suite to customers, said that "IBM is showing brilliant leadership in the cognitive era."

The data products being introduced to Bluemix, in addition to IBM’s place in the cognitive cloud marketplace and the Watson practice, help trigger engagements for the company's channel, he said.

A common refrain from customers, Kocolowski said, is they see data tools maturing and even though they are increasingly confident in the capabilities of those technologies, they struggle with the sheer volume of data available to them.

One major addition to Bluemix is Compose Enterprise, a product of IBM's 2015 Compose.io acquisition. The new Database-as-a-Service enables users to rapidly deploy open-source databases in a repeatable, consistent fashion. IBM has taken the containerized platform developed by the startup and put it in the hands of enterprise IT departments, he said.

That will help IT organizations better respond to the demands of line-of-business leaders, Kocolowski said. The second major addition to Bluemix addresses what IBM feels is a lot of confusion around graph databases, he said.

IBM is now operating a fully managed TinkerPop service called IBM Graph. The foundation of IBM's analytics platform is Apache Spark, and IBM has released complementary services to the core Spark engine.

One is Predictive Analytics, a service that helps customers on-ramp to working with machine-learning and multivariate data analysis. The service makes it easier for developers to build machine-learning models that deliver more accurate predictions, without needing to turn to actual data scientists.

There's a lot of budding data scientists out there, he said, and "rather than throwing them in the deep end, we're trying to get them comfortable with the notion of how machine learning works and how to extract a signal from a volume of data."

To complement the new services, IBM has launched Analytics Exchange, a repository of public data sets that users can pull into their own environment for analysis or integration with their applications.

Analytics Exchange will include a catalog of more than 150 data sets and IBM will keep adding more, as well as providing users with methods to publish data sets to share internally.

"One of the smartest ways to get personalization in the world is by taking cloud platforms and infusing them with cognition," Goodman said. "IBM is moving closer and closer to that type of access to data, methodology and instrumentation. This can indeed change the cloud and make it able to be tapped like never before."

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