Artificial intelligence is no longer just a buzzword — AI-based insights reshape how enterprises operate and make critical decisions. Yet, according to a global survey conducted by Teradata Corp., 40% of C-suite and AI decision-maker respondents don’t believe their company’s data can deliver accurate outcomes.
For Teradata, everything starts with trusted data, beginning with its founding in 1979, Dan Spurling, senior vice president of product management at Teradata, told theCUBE in an exclusive interview. Over the past five decades, the company has grown into a global leader in data analytics, helping businesses turn their data into insights they can trust. Today, Teradata’s Trusted AI initiative undergirds its mission, ensuring companies have the reliable, scalable and actionable data they need to drive more intelligent business decisions.
“The industry has always known that insights come from trusted data and that there’s no way you can continue gaining insights from data you don’t trust,” Spurling said. “Eventually, people will decide that this is not a source of information for them because they can’t trust it.”
Over the past year, Teradata has announced significant product enhancements to its VantageCloud Lake and ClearScape Analytics products, boosting its advanced analytics capabilities and making AI-driven insights more accessible to businesses. The company has also forged alliances with major players such as Amazon Web Services, Google Cloud, and Microsoft Azure. These alliances further empower Teradata to deliver scalable, cloud-native solutions that integrate seamlessly to generate AI-based insights for faster, data-driven decision-making.
Most recently, at its Possible 2024 event, Teradata announced that it will collaborate with Nvidia Corp. to integrate Nvidia’s graphics processing unit technology, accelerating AI and machine workloads. This collaboration further solidifies Teradata’s position as a leader in AI innovation, according to Spurling.
“As we move forward, it’s not just about having data; it’s about having the right data, integrated and harmonized, to drive AI success,” he explained. “Businesses can’t afford to operate in silos anymore. They need seamless integration.” (* Disclosure below.)
Integrated trusted data delivers reliable AI-based insights
In today’s AI-driven world, businesses can’t afford to make decisions based on unreliable or incomplete data. Trusted AI requires reliable, integrated and harmonized data across systems, ensuring that the insights generated can guide meaningful business outcomes.
“AI was probably literally a glimmer or a spark in someone’s eye back in 1979,” Spurling said. “But I think the idea of gaining insights from data and being able to do it in a simple, lower-cost and more readily accessible way was always the answer.”
Teradata has built its AI initiatives around the principle that data quality is non-negotiable, according to Spurling. AI models are only as good as the data they work with, and without accuracy and reliability, businesses risk making poor decisions. Trusted AI means that every data point contributing to a decision has been vetted, ensuring that models function optimally.
“As soon as you start to lose trust in your data, the insights you can draw from that data are compromised. You have to know that your data is clean, aligned across your systems and delivering consistent results across the board.”
During an exclusive interview at Possible 2024, Meeta Vouk, vice president of product management, AI and analytics at Teradata, talked with theCUBE about a real-world example showcasing the results organizations can achieve when AI is paired with trusted data. A project with a large retailer that managed 160,000 products leveraged Teradata’s AI solutions and integrated trusted data into their marketing strategies, hyper-personalizing four million customer marketing emails. As a result, the company saw a 28% uplift in sales, demonstrating the powerful impact of accurate data and AI-based insights on customer engagement.
As Vouk’s example highlights the tangible business outcomes from using trusted data and AI, Spurling reinforces Teradata’s overarching commitment to building AI models that drive confident decision-making across organizations. “Our focus has always been on empowering companies to make decisions confidently, knowing that their data is accurate and that their AI models are running on solid foundations,” he noted.
From silos to synergy: Teradata’s open, connected ecosystem
One of the critical differentiators for Teradata is its ability to integrate seamlessly with third-party tools and platforms, according to Spurling. In today’s enterprise environment, businesses rely on a variety of platforms, from cloud services to analytics tools. Teradata recognizes this need and has developed an open environment that integrates with industry-standard tools such as Anaconda Inc., DataRobot Inc. and Hugging Face Inc. This ensures that businesses can access and analyze data without vendor lock-in.
“So, companies have the data prep,” Spurling said. “If they want to pull in an external best-of-breed or industry-standard model, we can pull that natively into Teradata and run it against data in Teradata. We can also provide fine-tuning on that model, and when a customer is happy with the model, they can publish it into production.”
Another major component of Teradata’s approach is the use of open table formats, Spurling told theCUBE during an interview at Possible 2024. Organizations often struggle to provide easy access to trusted data due to the fragmentation of information across systems.
“We’re seeing open table formats emerge as a natural solution to this challenge of data silos, giving customers choice, flexibility and options,” Spurling said. “And it’s making us, on the provider side, truly compete — not on just whether you’re stuck in an isolated environment, but do you really have the best engine? Do you really have the performance? And we are so confident in our engine, so we said, ‘Bring it.’”
Teradata’s commitment to maintaining an open and connected ecosystem allows customers to use their preferred tools while capitalizing on Teradata’s powerful engine and workload management capabilities.
“We’re going to try to leverage the best-of-breed tools that end users are already using, whether for data movement, storage or placement,” Spurling said. The mission is to “really build an open and connected ecosystem of ‘Use the tool you love to use [and] get all the power and efficiency of Teradata’s engine.’”
Teradata’s strategy also enables diverse personas within an organization to collaborate and leverage the tools they are most comfortable with, Spurling noted. Whether a data scientist prefers working with Python or a business analyst uses a low-code platform, Teradata’s infrastructure supports these varying needs without forcing teams into a one-size-fits-all approach, fostering innovation across different enterprise personas.
“By supporting tools such as Python, structured query language, the R programming language and Visual Studio code, Teradata empowers different enterprise personas — from data scientists to engineers — to use their preferred coding environments,” Spurling said.
Scaling AI: Turning concepts into real-world solutions
Scaling AI projects poses significant challenges as organizations grow and deal with increasingly complex workflows. Moving AI from ideation to production requires solutions that not only build AI models efficiently, but also scale them to meet the evolving demands of real-world applications.
Teradata addresses these scaling challenges through ClearScape Analytics and GPU integration, according to Spurling. These tools enable faster training and inference, making it easier to manage the large data sets and advanced models required for AI at scale. Additionally, Teradata’s infrastructure supports organizations in deploying AI projects faster and more accurately.
A key element of Teradata’s scalable AI approach is its bring-you-own-model capability. With BYOM, enterprises can integrate pre-built models, including large language models, into Teradata’s platform. This flexibility allows companies to run external AI models seamlessly without rebuilding them, reducing time-to-market and supporting quicker AI deployment, according to Spurling.
This streamlined, scalable AI strategy is vital for enterprises looking to stay competitive in an AI-driven world, offering the flexibility and power needed to support advanced AI-based insights and initiatives.
“Our goal is to make scaling AI accessible to all our customers, no matter the size of their operation,” Spurling noted. “Whether it’s citizen data scientists or advanced teams, we provide the infrastructure and tools that move AI projects from concept to production.”
(* Disclosure: TheCUBE is a paid media partner for the Teradata Possible 2024 event. Neither Teradata, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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