Teradata integrates with DataRobot to aid AI development
The partnership provides the data management and analytics vendor's users with more choice so that they can develop advanced models and applications with their preferred tools.
Teradata on Tuesday unveiled a new integration with DataRobot that will enable customers to develop AI models and applications using DataRobot's AI capabilities within Teradata's VantageCloud platform.
Based in San Diego, Teradata is a longtime analytics and data management vendor that provides VantageCloud for storing and preparing data, and ClearScape Analytics for business intelligence. DataRobot, meanwhile, is a Boston-based AI vendor providing both traditional AI models and generative AI models that customers can use in concert with their proprietary data.
With enterprise interest in AI -- including generative AI -- exploding, the integration between the vendors is beneficial to each's customers, given that DataRobot takes a different approach to AI and machine learning analyses than Teradata's approach, according to David Menninger, an analyst at ISG's Ventana Research.
Teradata already supports AI and machine learning, but takes a programmatic approach, he noted. DataRobot, conversely, provides a graphical user experience. As a result, the vendors' tools complement each other.
Data scientists have their preferred approaches. The partnership allows Teradata to support a wider variety of users, and it provides DataRobot customers tighter integration with a robust platform for executing their analyses.
David MenningerAnalyst, ISG's Ventana Research
"Data scientists have their preferred approaches," Menninger said. "The partnership allows Teradata to support a wider variety of users, and it provides DataRobot customers tighter integration with a robust platform for executing their analyses."
In addition to DataRobot, Teradata has similar integrations with AI vendors H2O and Dataiku, as well as the open source PMML and ONNX AI and machine learning models.
"With more to come," said Michael Riordan, Teradata's senior director of product management for data science and analytics.
Teradata first launched VantageCloud and ClearScape Analytics in August 2022, just three months before OpenAI's ChatGPT hit the market and sparked the ongoing surge of interest in both generative AI and traditional AI.
Since then, Teradata -- like many other data management and analytics vendors -- has made AI a focus of its product development. For example, in April the vendor partnered with Anaconda to improve open source support for AI innovation in VantageCloud. Later that same month, Teradata introduced support for open table formats Apache Iceberg and Delta Lake to build up its ecosystem for AI development.
The integration
Data management and analytics vendors have made generative AI a focus of product development since the launch of ChatGPT, given generative AI's potential to enable nontechnical workers to work with data as well as make data experts more efficient.
Analytics use within organizations has been stagnant for about two decades, hovering at around a quarter of all employees. The simple reason is that analytics and data management platforms are hard to use, requiring coding to carry out most tasks and data literacy training to interpret results.
Natural language processing and low-code/no-code tools had promised to broaden analytics use, but they were limited in their capabilities. NLP tools are held back by small vocabularies, while low-code/no-code features only enable users to carry out basic tasks.
Generative AI, however, enables true natural language interactions because large language models have vocabularies the size of a dictionary. In addition, they can infer intent. That combination, when combined with an enterprise's proprietary data, enables almost anyone to query and analyze data to inform decisions.
Meanwhile, by reducing coding requirements, generative AI also frees data experts from time-consuming, repetitive tasks and enables them to do more with their time.
Teradata's new integration with DataRobot enables organizations to simplify combining data with AI and machine learning by applying DataRobot's prebuilt models to their data in VantageCloud. The integration can be accessed through Teradata's ClearScape Analytics Bring Your Own Model (BYOM) feature, which enables customers to select the model of their choice from the vendor of their choice for AI development.
Users who select one of DataRobot's models will be able to import and operationalize DataRobot's prebuilt AI models to accelerate their own AI development efforts in a way that will benefit existing Teradata users, according to Donald Farmer, founder and principal of TreeHive Strategy.
"This will be a useful technology for Teradata users ... to build models more quickly and score them more efficiently than they are currently doing," he said.
Specific benefits and features of the integration include the following:
Providing data scientists with greater choice to use their preferred platforms when developing predictive AI, generative AI and machine learning models and applications.
Model deployment in a secure, trusted environment that includes all cloud providers and on-premises, and that can scale to meet enterprise needs while providing a clear cost structure.
With BYOM, access from within VantageCloud not only to models provided by DataRobot, but to models an enterprise might have developed on its own in DataRobot.
While the integration is useful for existing Teradata customers, the true significance of any Teradata moves lies in whether they can attract new customers, according to Farmer.
He noted that Teradata has been overshadowed by Snowflake, which provides similar data management and analytics capabilities within its platform, and Teradata has struggled to gain new users. Snowflake, however, changed CEOs in March and then was the victim of a cyberattack in late May.
Despite investing more heavily in AI under new CEO Sridhar Ramaswamy and getting hacked through unsecured customer logins rather than faulty security, confidence in the vendor has taken a hit, and its stock has languished well below its value before former CEO Frank Slootman stepped down.
Snowflake's struggles could provide Teradata with an opportunity.
"The big question around Teradata is, how does it find new greenfield customers?" Farmer said. "[The integration] is an advantage to existing customers ... but will this make Teradata an attractive data platform for DataRobot customers? If that can happen, then this is a significant move."
If not, however, the integration will provide easier-to-use capabilities for existing Teradata customers, but have no greater meaning, he continued.
"Then it will be nice on paper and good to have, but it won't move the needle," Farmer said. "If, finally, this enables Teradata to get more new customers because they can be a robust enterprise data platform deeply integrated for DataRobot users, that's potentially different and better."
Menninger likewise said that while Teradata provides robust data management and analytics tools, the vendor has been overshadowed by Snowflake and Snowflake rival Databricks.
Teradata continues to invest in a modern architecture and can be used with all the major cloud providers. As a result, from a technical perspective, Teradata matches up well with rival vendors and is rated highly in ISG's Buyers Guides for data platforms and AI platforms, Menninger noted.
"They have been eclipsed in terms of revenue by vendors such as Snowflake and Databricks, [but] they are still considered a leader in terms of functionality, manageability, scalability and reliability," he said.
Future direction
One of Teradata's main goals is to be open and connected by integrating with various other vendors and operating in numerous different environments to provide customers with choice, according to Riordan.
The integration with DataRobot represents an addition to that openness and connectedness.
Going forward, Teradata plans to continue finding ways to enable enterprises to operationalize AI and machine learning initiatives through VantageCloud and ClearScape Analytics, Riordan said. Specific initiatives include improving ClearScape's deep learning and language model capabilities, along with upgrades to the platform's user experience for model development and deployment.
Continued focus on AI is wise, according to Menninger. In particular, a focus on developing generative AI tools such as its Ask.ai assistant that simplify using its platform is important to keep up with what other vendors are providing.
"All data platform providers are in the process of adding GenAI tooling to make it easier to use their products," Menninger said. "Teradata has started down this path ... but we expect that the competitive bar will continue to rise as more and more data and analytic functionality is assisted with GenAI technology."
Farmer, meanwhile, stressed that Teradata needs to do more to grow its customer base.
Rather than focus on adding new customers, the vendor in recent years has focused on meeting the needs of its existing customers, according to Farmer. While sound strategy for a time, at some point that needs to change. And with Snowflake scuffling, now is perhaps that time.
"I would like to see them expand into a greenfield strategy again and start to become a more agile platform for people who need to scale their enterprise-class data management," Farmer said. "I think they have a window of opportunity because of Snowflake's problems. If they can act quickly, they have an opportunity."
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
