Teradata Unveils AI-Driven Data Analytics Innovations and NVIDIA Partnership at Possible 2024

Three AI-driven data analytics insights from Teradata Possible 2024 - SiliconANGLE

Integrating AI-driven data analytics into enterprise systems presents both opportunities and challenges. As businesses embrace artificial intelligence to drive efficiency and innovation, they face a growing need to balance technological advancements with robust data strategies. For companies such as Teradata Corp., this focus on scalable AI and reliable data management is vital to maintaining a competitive edge. By leveraging strategic partnerships, Teradata enhances its AI capabilities while ensuring that its data infrastructure supports innovation and operational resilience. "Over the last 40 years, but especially in the last four years, we've been able to pivot the company to develop a trusted hybrid platform for AI at scale," Steve McMillan (pictured), chief executive officer at Teradata, told theCUBE during the event. "And we are delivering those solutions right here, right now ... leveraging the partnerships we've just announced with folks like Nvidia [Corp]. If we can really leverage the AI capabilities and our massively parallel architecture, our core technologies around workload management [and] pre-optimization to really deliver in that AI world, I think it's going to make a massive difference to the company." During Teradata Possible 2024, theCUBE Research's Rob Strechay and co-host Savannah Peterson spoke with industry leaders who provided insights into Teradata's expanding ecosystem. Discussions also focused on the company's recent technological innovations and Trusted AI initiative, which drive its strategy to build scalable AI platforms that enable organizations to access AI's full potential in their data-driven decision-making processes. Here are three key insights you may have missed from theCUBE's coverage of Possible 2024: Teradata Corp. has been steadily transitioning from its legacy systems to a more agile, hybrid AI platform with a strong focus on AI-driven data analytics, emphasizing the importance of sustainability and flexibility in data management. The company leverages its partnerships with cloud giants such as Google and Amazon Web Services to extend its hybrid analytics capabilities and bring AI-driven insights to customers right where their data resides, according to McMillan. "Our Teradata approach in the past has been: 'Bring all of your data into our ecosystem,'" he told theCUBE during the event. "But we've recognized the fact that data is going to be in many places in the customer ecosystem, and we should move the query engine to the data rather than moving the data to the query engine, which is a very, very different approach." A central pillar of Teradata's strategy is its use of open table formats and AI-driven data analytics to break down silos and enhance data accessibility across the enterprise, according to Dan Spurling, senior vice president of product management at Teradata." A central pillar of Teradata's strategy is its use of open table formats and data democratization to break down silos and enhance data accessibility across the enterprise, according to Dan Spurling, senior vice president of product management at Teradata. This approach boosts operational efficiency and supports the seamless integration of AI capabilities into various business processes. "Do you really need to [build a complex data mesh], or do you need to figure out how to have some type of trusted way to centralize this data and bring the engines or the tools to the data?" he told theCUBE during the event. "We're seeing open table formats emerge as a natural solution to this challenge of data silos and then giving customers choice ... flexibility ... and options," he said. With AI increasingly driving business decisions, Teradata focuses on making AI integration more straightforward and scalable, according to McMillan. The company's infrastructure supports hybrid AI, enabling data scientists to run AI models efficiently on central processing units and graphics processing unit infrastructures without compromising performance. "The real magic of Teradata is [its] massively parallel architecture, and the way that we orchestrate workloads enables us to run complex AI and gen AI workloads just as well on a [central processing unit] infrastructure as they can run on a [graphics processing unit] infrastructure and that's a massive leap ahead for us," McMillan said. Teradata's innovative approach emphasizes technical capabilities and commitment to trusted, ethical AI practices, McMillan noted. The company's AI infrastructure aims to reduce energy consumption and optimize performance, positioning Teradata as a sustainable leader in AI technologies. "If you think about the energy usage predictions for these huge inference engines, you start to get to the point [that] you've got to be able to run it efficiently and effectively," he said. Here's the complete video interview with McMillan, part of SiliconANGLE's and theCUBE Research's coverage of Teradata Possible: Security, compliance and regulation concerns grow as AI becomes more integrated into enterprise operations, which introduces new cybersecurity challenges as malicious actors look for vulnerabilities in AI systems to exploit. Teradata's focus on AI security highlights the importance of creating robust defenses against these evolving threats, according to Billy Spears, chief information security officer of Teradata. "Today we have these cloud products, two amazing products customers can choose from," he told theCUBE during the event. "And inside of that, we've built things like threat models. That's all the behind-the-scenes risks. We think about it so you don't have to." Compliance in the area of AI-driven data analytics is becoming increasingly complex due to the global nature of data transfer and stringent regulatory standards, according to Spears. Companies must navigate frameworks such as the Payment Card Industry Data Security Standard, the Health Insurance Portability and Accountability Act and the International Organization for Standardization to maintain secure operations. "It's complicated, and there are so many regulations, rules, and frameworks ... there are so many different sets of controls," Spears noted. "We're talking hundreds if not thousands of controls you have to think about. On-prem, you have to manage that all by yourself. In the cloud, we do all that homework for you, which is phenomenal." Looking ahead, the Digital Operational Resilience Act is set to raise the bar for financial services, demanding new levels of cloud recovery and data protection capabilities. Teradata is already preparing for these upcoming changes by aligning its solutions to meet these stringent requirements. "We know exactly where this DORA is, and it is probably the most stringent regulation since we've seen [the General Data Protection Regulation]," Spears said. "We're already ahead of those things. We have all these certifications across all of our product lines now, and we're still achieving greater heights." Here's the complete interview with Spears, part of SiliconANGLE's and theCUBE Research's coverage of Teradata Possible: Teradata's recent partnership with Nvidia has expanded its AI capabilities, integrating Nvidia's AI-accelerated technologies into Teradata's ClearScape Analytics platform, according to Meeta Vouk, vice president of product management AI and analytics of Teradata. This collaboration aims to provide Teradata's customers with the tools they need to efficiently manage and deploy AI-driven data analytics workloads, enhancing both speed and scalability in their data analytics efforts. "We are saying, 'Bring AI to the data rather than move the data to AI,'" Vouk told theCUBE during the event. "If you're a data scientist and you want to experience all of the capabilities that ClearScape Analytics offers, you can quickly spin up a node, experiment, or get some results out of AI and then shut it down." Teradata's strategy emphasizes trusted data when deploying AI models, leveraging its partnerships to create seamless, scalable AI solutions, according to Jacqueline Woods, chief marketing officer of Teradata. By focusing on high-quality, curated datasets, Teradata aims to address one of the most critical challenges in AI -- the need for accurate, reliable data to drive meaningful insights. "We are the golden record keeper," Woods told theCUBE during the event. "One of the things that companies rely upon us for is our capability of having these trusted records, which we call golden records. And to understand that what we do is what people are looking for, particularly in this time of AI." Teradata has also partnered with NewtonX Inc. to tackle challenges such as aligning AI initiatives with business strategies. Companies often experience a gap between ambition and execution due to siloed data, according to Leon Mishkis, chief operating officer of NewtonX. Teradata's trusted data solutions help bridge this gap, ensuring AI delivers tangible outcomes. "We've been partnering with Teradata over the last few years on various research initiatives around brand health, around establishing the right [key performance indicators] for the brand," Mishkis told theCUBE during the event. "Most recently, we launched a very exciting research in the AI space, specifically around trust in AI of senior executives." The impact of these AI innovations is already visible in real-world applications, according to Vouk. For example, a large retailer using Teradata's AI-driven data analytics capabilities was able to personalize a marketing campaign that reached four million customers, resulting in a 28% increase in sales. "The use cases are getting so complex, and to solve for them in really meaningful ways, you're going to need multiple models and multiple capabilities," Vouk said. "It's not really AI for AI's sake or gen AI for gen AI's sake. It's really fit-for-purpose AI." Here is the complete video interview with Woods and Mishkis, part of SiliconANGLE's and theCUBE Research's coverage of Teradata Possible: To watch more of theCUBE's coverage of the Teradata Possible 2024 event, here's our complete event video playlist:

Teradata AI offerings: accelerate ROI with new solutions - SiliconANGLE

Nvidia partnership and perks for ClearScape fuel Teradata AI offerings This week, Teradata Corp. unveiled new capabilities for VantageCloud Lake and ClearScape Analytics designed to speed the return on investment from generative AI use cases. The new Teradata AI offerings will enable customers to add their own large language models and take advantage of small or mid-size open AI solutions. "If you're a data scientist and you want to experience all of the capabilities that ClearScape Analytics offers, you can quickly spin up a node, you can experiment or ... get some results out of AI, and then you can shut it down," said Meeta Vouk (pictured), vice president of product management, AI and analytics, at Teradata. "We are saying, 'Bring AI to the data rather than move the data to AI.'" Vouk spoke with theCUBE Research's Rob Strechay and co-host Savannah Peterson at Teradata Possible during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed a new partnership with Nvidia Corp. and growing use cases for AI. (* Disclosure below.) Tuesday's announcement was accompanied by the news that Teradata will collaborate with Nvidia in an integration with the chipmaker's AI full-stack accelerated computing platform. The collaboration will include Nvidia NIM, inference microservices that accelerate the deployment of foundation models in the cloud or data center. "We are saying we will meet our customers where they are, whether they're on-prem or in the cloud," Vouk said. "We believe in hybrid AI, and if we need graphics processing units to solve for something, we will use GPUs. If you need central processing units, we'll use CPUs. There are certain use cases which will always need GPUs, fine-tuning of models, [and creation of] vector embedding, so we really wanted to support our customers for those needs. That's the idea behind it." Vouk discussed a number of use cases that have featured Teradata AI offerings in recent months as customers continue to implement AI solutions from prototype to production. She described the example of a large retailer with 160,000 products that sent a marketing email to four million customers. AI was able to personalize the campaign and achieve a 28% uplift in sales, according to Vouk. "The use cases are getting so complex, and to solve for them in really meaningful ways, you're going to need multiple models and multiple capabilities," Vouk said. "It's not really AI for AI's sake or gen AI for gen AI's sake. It's really fit-for-purpose AI." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of Teradata Possible:

Teradata harnesses its history of parallel architecture for AI era - SiliconANGLE

Teradata positions itself as sustainable hybrid AI platform Teradata Corp. is embracing both legacy and innovation as the company looks to build on its long history of parallel architecture to become a trusted artificial intelligence platform. Recently, Teradata has leveraged its partnerships with cloud giants such as Google and Amazon Web Services to offer hybrid data analytics. According to its Chief Executive Officer, Steve McMillan (pictured), the company is prepared for the AI era. "Our Teradata approach in the past has been: bring all of your data into our ecosystem," he said. "But we've recognized the fact that data is going to be in many places in the customer ecosystem, and we should move the query engine to the data rather than moving the data to the query engine, which is a very, very different approach. It's a patented capability that we have in the Teradata ecosystem that allows us to distribute that workload, and we talk about meeting our customers where they want to be met." McMillan spoke with theCUBE Research's Rob Strechay and co-host Savannah Peterson at Teradata Possible during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the future of Teradata and what customers look for in an AI company. (* Disclosure below.) As it has ventured into the AI sphere, Teradata has focused on creating a trusted, ethical and sustainable platform to combat issues of AI hallucination. Large language models can also be an energy sinkhole, a challenge that McMillan claims to have answered. "If you think about the energy usage predictions for these huge inference engines, you start to get to the point [that] you've got to be able to run it efficiently and effectively," he said. "The real magic of Teradata is [its] massively parallel architecture, and the way that we orchestrate workloads enables us to run complex AI and gen AI workloads just as well on a [central processing unit] infrastructure as they can run on a [graphics processing unit] infrastructure, and that's a massive leap ahead for us." Teradata announced a new partnership with Nvidia Corp. as part of its strategy for AI innovation. The companies will be able to combine Teradata's software and orchestration abilities with Nvidia's GPU infrastructure. "Over the last 40 years, but especially in the last four years, we've been able to pivot the company to develop a trusted hybrid platform for AI at scale," McMillan said. "And we are delivering those solutions right here, right now, today, leveraging the partnerships we've just announced with folks like Nvidia. If we can really leverage the AI capabilities and our massively parallel architecture, our core technologies around workload management [and] pre-optimization to really deliver in that AI world, I think it's going to make a massive difference to the company." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of Teradata Possible:

Teradata Makes Real-World GenAI Easier, Speeds Business Value By Investing.com

New bring-your-own LLM capability enables Teradata customers to simply and cost-effectively deploy everyday GenAI use cases with NVIDIA (NASDAQ:NVDA) AI to deliver flexibility, security, trust and ROI New integration with the full-stack NVIDIA AI platform delivers accelerated computing LOS ANGELES--(BUSINESS WIRE)--TERADATA POSSIBLE " Teradata (NYSE: TDC) today announced new capabilities for VantageCloud Lake and ClearScape Analytics that make it possible for enterprises to easily implement and see immediate ROI from generative AI (GenAI) use cases. As GenAI moves from idea to reality, enterprises are increasingly interested in a more comprehensive AI strategy that prioritizes practical use cases known for delivering more immediate business value " a critical benefit when 84 percent of executives expect ROI from AI initiatives within a year. With the advances in the innovation of large language models (LLMs), and the emergence of small and medium models, AI providers can offer fit-for-purpose open-source models to provide significant versatility across a broad spectrum of use cases, but without the high cost and complexity of large models. By adding bring-your-own LLM (BYO-LLM), Teradata customers can take advantage of small or mid-sized open LLMs, including domain-specific models. In addition to these models being easier to deploy and more cost-effective overall, Teradata's new features bring the LLMs to the data (versus the other way around) so that organizations can also minimize expensive data movement and maximize security, privacy and trust. Teradata also now provides customers with the flexibility to strategically leverage either GPUs or CPUs, depending on the complexity and size of the LLM. If required, GPUs can be used to offer speed and performance at scale for tasks like inferencing and model fine-tuning, both of which will be available on VantageCloud Lake. Teradata's collaboration with NVIDIA, also announced today, includes the integration of the NVIDIA AI full-stack accelerated computing platform, which includes NVIDIA NIM, part of the NVIDIA AI Enterprise for the development and deployment of GenAI applications, into the Vantage platform to accelerate trusted AI workloads large and small. Teradata customers want to swiftly move from exploration to meaningful application of generative AI, said Hillary Ashton, Chief Product Officer at Teradata. ClearScape Analytics' new BYO-LLM capability, combined with VantageCloud Lake's integration with the full-stack NVIDIA AI accelerated computing platform, means enterprises can harness the full potential of GenAI more effectively, affordably and in a trusted way. With Teradata, organizations can make the most of their AI investments and drive real, immediate business value. Real-World GenAI with Open-Source LLMs Organizations have come to recognize that larger LLMs aren't suited for every use case and can be cost-prohibitive. BYO-LLM allows users to choose the best model for their specific business needs, and according to Forrester, 46 percent of AI leaders plan to leverage existing open-source LLMs in their generative AI strategy. With Teradata's implementation of BYO-LLM, VantageCloud Lake and ClearScape customers can easily leverage small or mid-sized LLMs from open-source AI providers like Hugging Face, which has over 350,000 LLMs. Smaller LLMs are typically domain-specific and tailored for valuable, real-world use cases, such as: Teradata's commitment to an open and connected ecosystem means that as more open LLMs come to market, Teradata's customers will be able to keep pace with innovation and use BYO-LLM to switch to models with less vendor lock-in. GPU Analytic Clusters for Inferencing and Fine-Tuning By adding full-stack NVIDIA accelerated computing support to VantageCloud Lake, Teradata will provide customers with LLM inferencing that is expected to offer better value and be more cost-effective for large or highly complex models. NVIDIA accelerated computing is designed to handle massive amounts of data and perform calculations quickly, which is critical for inference - where a trained machine learning, deep learning or language model is used to make predictions or decisions based on new data. An example of this in healthcare is the reviewing and summarizing of doctor's notes. By automating the extraction and interpretation of information, they allow healthcare providers to focus more on direct patient care. VantageCloud Lake will also support model fine-tuning via GPUs, giving customers the ability to customize pre-trained language models to their own organization's dataset. This tailoring improves model accuracy and efficiency, without needing to start the training process from scratch. For example, a mortgage advisor chatbot must be trained to respond in financial language, augmenting the natural language that most foundational models are trained on. Fine-tuning the model with banking terminology tailors its responses, making it more applicable to the situation. In this way, Teradata customers could see increased adaptability of their models and an improved ability to reuse models by leveraging accelerated computing. Availability ClearScape Analytics BYO-LLM for Teradata VantageCloud Lake will be generally available on AWS in October, and on Azure and Google (NASDAQ:GOOGL) Cloud in 1H 2025. Teradata VantageCloud Lake with NVIDIA AI accelerated compute will be generally available first on AWS in November, with inference capabilities being added in Q4 and fine-tuning availability in 1H 2025. About Teradata At Teradata, we believe that people thrive when empowered with trusted information. We offer the most complete cloud analytics and data platform for AI. By delivering harmonized data and trusted AI, we enable more confident decision-making, unlock faster innovation, and drive the impactful business results organizations need most. See how at Teradata.com. The Teradata logo is a trademark, and Teradata is a registered trademark of Teradata Corporation (NYSE:TDC) and/or its affiliates in the U.S. and worldwide. View source version on businesswire.com: https://www.businesswire.com/news/home/20241008518850/en/

Teradata to Bring World-Class AI Capabilities to Large Enterprises and Hybrid Environments in Collaboration with NVIDIA By Investing.com

LOS ANGELES--(BUSINESS WIRE)--TERADATA POSSIBLE " Teradata (NYSE: TDC) today announced a new collaboration with NVIDIA to enhance the Teradata Vantage platform with NVIDIA AI to benefit large, global organizations that leverage both public and/or private clouds. Teradata is integrating NVIDIA NeMo and NVIDIA NIM microservices into the Vantage platform to accelerate AI workloads and support the development of foundation and customized large language models (LLMs), agentic workflows and retrieval-augmented generation (RAG) applications. Customers can also deploy their own custom models through NVIDIA AI Enterprise, an end-to-end software platform that offers enterprise-grade security, support and stability, to drive ROI from generative AI use cases. In addition to multiple NVIDIA software integrations, Teradata's platform will offer NVIDIA accelerated computing infrastructure. The first implementation of this effort was announced today as part of Teradata's new support for both small language models (SLMs) and open LLMs in VantageCloud Lake. The new bring-your-own LLM capability can take advantage of NVIDIA AI accelerated computing platform clusters for tasks like LLM inferencing and model fine-tuning. Teradata customers are not casual users of their data and analytics, so we're thrilled to be engaged with NVIDIA to leverage our strong and trusted foundation for innovative AI use cases. By integrating NVIDIA's accelerated computing architecture into our Vantage platform, Teradata customers will be able to accelerate their use of AI at scale, in the environment of their choice, delivering unprecedented business value, said Hillary Ashton, Chief Product Officer at Teradata. NVIDIA NeMo Retriever, a collection of NVIDIA NIM microservices that enables organizations to seamlessly connect custom models to diverse business data and deliver highly accurate responses, will also be offered on Teradata VantageCloud, enabling accelerated search and RAG applications to leverage existing customer data. Teradata will provide access to NVIDIA NIM microservices, part of NVIDIA AI Enterprise, for Teradata VantageCloud customers before expanding the offering to hybrid customers globally. Data is the foundation of generative AI applications, enabling the development of today's highly customized applications, said Pat Lee, Vice President of Strategic Enterprise Partnerships at NVIDIA. By integrating NVIDIA AI Enterprise, Teradata is providing developers a high-performance, full-stack platform that offers the security, stability and support enterprises require. Availability Teradata VantageCloud Lake NVIDIA AI accelerated compute will be generally available first on AWS in November, with inference capabilities being added in Q4 and fine-tuning availability in 1H 2025. NVIDIA AI Enterprise integrations will be coming in 2025. About Teradata At Teradata, we believe that people thrive when empowered with trusted information. We offer the most complete cloud analytics and data platform for AI. By delivering harmonized data and trusted AI, we enable more confident decision-making, unlock faster innovation, and drive the impactful business results organizations need most. See how at Teradata.com. The Teradata logo is a trademark, and Teradata is a registered trademark of Teradata Corporation (NYSE:TDC) and/or its affiliates in the U.S. and worldwide

Open LLMs: Teradata and Nvidia revolutionize enterprise AI - SiliconANGLE

Teradata and Nvidia team up to drive enterprise AI with customizable open LLMs As large language models continue fueling the artificial intelligence fire, the need for open LLMs, enhanced customization and adaptability -- which boosts customer centricity -- is growing. Since open LLMs can be fine-tuned and adapted for domain-specific tasks, Teradata Corp. has set things in motion through its Bring Your Own Large Language Model feature, designed to drive faster enterprise return on investment with AI by deploying real-world generative AI use cases with flexibility, ease and trust, according to Hillary Ashton (pictured), chief product officer of Teradata. "I think that our customers who are in the global 10,000 market really want to be able to manage their own environments," Ashton stated. "They're not going to be putting data out there in the public domain. They want to be able to bring their own large language model. They want to be able to customize it and tailor it, maybe make it small or medium size. Customer context really matters in the era of AI. BYO Large Language Models like or whatever works." Ashton spoke with theCUBE Research's Rob Strechay and co-host Savannah Peterson at Teradata Possible during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed why organizations need open LLMs, such as BYO LLMs, to create a more diverse, customizable and adaptable AI landscape. (* Disclosure below.) Given that leveraging open LLMs is a fast-emerging alternative and disruptive force, Teradata has joined hands with Nvidia Corp. This move is meant to foster AI usage and research, according to Ashton. "Teradata is known for so many things, but one of the biggest things that we're known for is our ability to scale with massive amounts of data and analytics, and who better than Nvidia and Teradata to come together to help our customers with large language models in this new era of AI?" she said. "Nvidia and their graphics processing units are going to help our customers scale beyond what they're able to do today with large language models." With data being the backbone of AI, aspects such as data meshing are important since they make AI better. This is because data meshing offers a decentralized approach to data management, and Teradata makes this a reality through QueryGrid, Ashton pointed out. "We have a product called QueryGrid, which lets our customers get data wherever it is," she explained. "If you want to do remote push-down processing, you can do that, which is very efficient. You don't have to relocate the data to do that ... [you can] extend that out to gen AI use cases, and I think it's even better because a lot of that unstructured data is sitting elsewhere. It's not in a highly structured environment." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of Teradata Possible: