Knime unveils collaboration hub for analytics, data science

New capabilities

Knime previously offered Knime Server as a complement to its open source analytics platform. Server enabled Knime Analytics Platform users to put security and data governance measures in place and move their data science and analytics assets into production.

But Knime Server was limited in its reach throughout an organization.

The tool -- which Knime is discontinuing, but will still support as customers transition to the new hub -- was localized to departments or groups of users. 

A single instance of Knime Business Hub, however, is cloud-native, and therefore can be managed in a centralized way and deployed across an entire organization to enable sharing and collaboration on data science and analytics projects.

And that ability to share and collaborate at scale is critical, according to Mike Leone, analyst at TechTarget's Enterprise Strategy Group.

Data science collaboration is a different beast compared to ... simply sharing files. This release of Knime Hub brings that much-needed level of collaboration to organizations and does this on a modern, cloud-native stack for operational efficiency.

Knime differs from many vendors by enabling not only data analysis, but also data science. And sharing and collaborating on best practices, data experimentation and workflow management are critical to the data science process, he noted.

"Data science collaboration is a different beast compared to ... simply sharing files," Leone said. "This release of Knime Hub brings that much-needed level of collaboration to organizations and does this on a modern, cloud-native stack for operational efficiency."

Similarly, Ritu Jain, chief marketing officer at Knime, noted that operational efficiency -- the ability to add scale to a single deployment -- is a critical aspect of the evolution from Knime Server to Knime Business Hub.

Knime customers now have the option to share data science and other analytics assets within their group, as they could with Server, or extend well beyond that limited number of users to share and collaborate with anyone in their organization.

"You no longer have marketing on one server and finance on another, and no way to connect them -- the Knime Hub brings the community together in one platform," she said. "That allows users to still have private spaces, or they can share something across the organization. You can expand use as much as you want, depending on your data infrastructure."

The community, meanwhile, was the genesis for the development of Knime Business Hub, according to Phil Winters, strategic advisor at Knime.

Two years ago, Knime launched its Community Hub to enable its Open Source users to share and collaborate on data science and analytics projects irrespective of their organization. Feedback from Community Hub users then led Knime to build a scalable commercial tool to enable enterprises to collaborate on data science and analytics projects and move those products into production.

"We're looking at the market, but mostly we're being driven by customer feedback," Winters said.

Jain added that enterprise customers were taking Knime's capabilities to build their own internal hubs, and seeing that, the vendor determined to build the Knime Business Hub itself.

"They were doing it internally and showing that there's a demand for scalable, cloud-native architecture," she said. "They didn't want to install another [instance of] Server, and then three months later after their needs grew, another. The demand for a scalable, cloud-native architecture was high, so it had to be done."

While the Business Hub is new to Knime and enables sharing and collaboration among all users within an organization, the hub concept is not unique to Knime.

Google earlier this year launched Analytics Hub, an exchange within its analytics platform where users can share and collaborate, and ThoughtSpot and Qlik are among other vendors that have added spaces to collaborate within their platforms.

However, as Leone noted, data science collaboration is more complex than file sharing and collaborative data analysis, so Knime Business Hub has the potential to be significant for users.

"Organizations need better ways to work together on these complex data science projects, and that includes simply getting started," he said. "With Knime Hub, there's the advantage of Knime having an open source model and giving organizations the flexibility they need to get started."

Pricing and future plans

A single deployment of the Knime Business Hub is more expensive than a single deployment of Knime Server.

But because Server had limited reach, and large enterprises often had to deploy Server multiple times, the actual cost for organizations to deploy the full breadth of Knime's analytics capabilities might decrease with the launch of the Business Hub.

Server was available in three tiers, with the Small option available for purchase on Azure or AWS, Medium available at $29,000 or 25,000 euros annually, and Large costing $52,000 or 45,500 euros annually.

Knime Business Hub is available at 62,500 euros per year for the Standard Edition and 140,000 euros for the Enterprise Edition; a SaaS version is available for 250 euros per month.

The Knime Analytics Platform is free.

And given the different tiers, Knime Hub is accessible to organizations of all sizes, according to Winters.

"Small to medium-sized organizations aren't so worried about really advanced things and don't even have the teams to think about them, so for individual departments and small and medium-sized businesses, what they want is the Teams offering," he said.

Standard, meanwhile, is priced at a level that most department heads can approve, and for large enterprises, the cost of the Enterprise Edition is affordable, Winters continued.

With the Knime Business Hub now generally available and Server being phased out, the vendor is continuing to work on its user experience, according to Winters. In addition, he noted that the continuous integration/continuous delivery (CI/CD) of machine learning and augmented intelligence is an ongoing focus.

That ongoing CI/CD focus is important for Knime, according to Leone.

"Supporting the management of the entire data science and AI lifecycle is a hot area right now," he said. "With the understanding that these projects are ever-changing and iterative, being able to support both the creation side of projects as well as the production side of projects will go a long way."