More than 65% of data science projects in large organizations fail, according to Teradata CEO, Steve McMillan. In this article, we explore the reasons behind these failures and the importance of trusted data for successful AI initiatives. Join us as we delve into the challenges organizations face and the solutions offered by Teradata to help organizations achieve real business outcomes with their data science projects.
The Challenges of Putting AI Models into Production
Discover the main challenges organizations face when trying to put AI models into production.
While many organizations have great ideas and develop models for AI projects, the main challenge lies in putting these models into production using real enterprise data and technology. According to Teradata CEO, Steve McMillan, more than 65% of data science projects fail due to this difficulty.
One of the key factors for successful AI implementation is the quality and trustworthiness of the data. Organizations need to ensure that their data is of high quality, has integrity, and can be relied upon for making accurate recommendations. Without trusted data, AI initiatives are likely to fall short of expectations.
Teradata has recognized this challenge and invested in integrating with platforms like AWS SageMaker to help organizations overcome it. They have developed a model ops capability in the Teradata platform, enabling customers to take their AI models from concept to production.
By harmonizing and utilizing data effectively, organizations can leverage AI capabilities to achieve real business outcomes.
Teradata's Cloud Transformation Journey
Learn about Teradata's cloud transformation journey and how it has impacted the company's offerings.
Teradata, a 40-year-old firm, faced challenges when it failed to invest in its cloud strategy. This led to a loss of industry imagination and a significant difference in revenue between cloud and on-premises solutions.
Recognizing the need for change, Teradata made a strategic shift and invested heavily in its cloud capabilities. They rearchitected their platform as a cloud-native solution, making it available on both AWS and Azure.
Through this transformation, Teradata has attracted talented engineers who have returned to the company, bringing their expertise and enthusiasm for the cloud-first approach. The company has made significant progress in the last three years, with hundreds of customers now using Teradata in the cloud.
Teradata aims to achieve half a billion dollars of recurring revenue in the cloud by the end of 2023 and over a billion dollars by 2025.
The Importance of Trusted Data for AI Success
Explore why trusted data is crucial for the success of AI initiatives.
Trusted data is the foundation for successful AI initiatives. Organizations must ensure that their data is of high quality, reliable, and can be trusted to make accurate recommendations.
Without trusted data, AI models may produce inaccurate or unreliable results, leading to failed projects. It is essential to have data governance processes in place to maintain data integrity and ensure its suitability for AI applications.
Teradata's focus is to help organizations harmonize and utilize their data effectively. By leveraging their platform, organizations can feed data science models, large language models, and AI capabilities to achieve real business outcomes.
Teradata's Transformation and Customer-Centric Approach
Learn how Teradata has transformed its offerings and prioritized customer success.
Teradata has undergone a complete transformation, starting with its technology and extending to its customer-centric approach. The company has rearchitected its platform as a cloud-native solution, making it available on major cloud platforms like AWS and Azure.
Teradata's focus is on helping customers get the most out of their data, regardless of its location. They provide a unified ecosystem that allows customers to access and utilize their data for real queries and real business outcomes.
The company has invested in its cloud strategy and aims to achieve significant recurring revenue in the cloud in the coming years. Teradata's transformation has positioned them as a leader in the industry, providing innovative solutions for data-driven organizations.
Conclusion
Successful implementation of AI projects requires more than just great ideas and models. It hinges on the ability to put those models into production using real enterprise data and technology. Organizations must prioritize the quality and trustworthiness of their data to achieve accurate and reliable AI recommendations.
Teradata has recognized these challenges and invested in cloud transformation to offer a cloud-native solution. By harmonizing and utilizing data effectively, organizations can leverage Teradata's platform to achieve real business outcomes. Teradata's customer-centric approach and focus on trusted data have positioned them as a leader in the industry.
FQA
Why do data science projects fail?
Data science projects often fail due to the difficulty of putting models into production using real enterprise data and technology. The lack of trusted data and data integrity can lead to inaccurate AI recommendations.
What is Teradata's cloud transformation journey?
Teradata recognized the need for change and invested heavily in its cloud capabilities. They rearchitected their platform as a cloud-native solution, making it available on major cloud platforms like AWS and Azure.
Why is trusted data important for AI success?
Trusted data is crucial for the success of AI initiatives. Without trusted data, AI models may produce inaccurate or unreliable results. Data governance processes are necessary to maintain data integrity and ensure its suitability for AI applications.
How has Teradata transformed its offerings?
Teradata has undergone a complete transformation, starting with its technology and extending to its customer-centric approach. They provide a unified ecosystem that allows customers to access and utilize their data for real queries and real business outcomes.
