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Discover Hidden Insights: Leveraging Machine Learning for Business Growth

In-database analytics functions can help you uncover patterns and insights within your data.

Steve Anderson
Steve Anderson
5. Dezember 2024 2 min Lesezeit
In a competitive marketplace, gaining a deeper understanding of your customers is critical to sustaining business growth. At Teradata, we empower companies to unlock hidden insights that not only provide competitive advantages but also drive meaningful business outcomes. 

Why advanced analytics matter

Leveraging advanced analytics is no longer optional—it’s essential. Teradata VantageCloud offers a comprehensive suite of in-database analytics functions designed to help businesses uncover hidden patterns and insights within their data. With our highly distributed processing capabilities, Teradata delivers unparalleled performance at scale, outperforming competitors like Databricks and Snowflake—particularly at large scale.

Powerful tools at your fingertips

VantageCloud includes ClearScape Analytics™, a tool that enables advanced analytics and AI/ML. Both offer efficiency, security, and cost effectiveness that set Teradata apart as a leader in Trusted AI. Additionally, our platform’s flexibility allows you to use other technologies, such as R or Python, at scale within our ecosystem and ensures that you can choose the tools that best fit your unique needs and use cases.

Example: K-means clustering for customer segmentation

K-means clustering can add value to your business. K-means clustering is a popular unsupervised machine learning algorithm, which automatically buckets similar items in groups or clusters. This algorithm is highly effective for customer segmentation, allowing you to group similar customers into distinct segments, gain a deeper understanding of your customers, and personalize your offerings to meet their needs. 

For instance, when I worked with a major league sports team, I utilized k-means clustering to analyze customer data. The insights were straightforward and useful, as the algorithm identified that families were purchasing more seats per game than any other customer segment. Equipped with this knowledge, I collaborated with sales and ticketing teams to create multiseat offers specifically designed for families, resulting in a significant increase in sales and engagement. 

K-means clustering can be done with ClearScape Analytics at large scale, thanks to in-database distributed processing.  

The ability to use the language or tool of your choice - such as R, Python, Teradata analytics functions, or LLM - at scale, without having to move or transfer your data, delivers enterprise value.

Driving business outcomes

This is just one of over 200 in-database analytics functions available with ClearScape Analytics, with each one designed to address specific business challenges. Whether you’re looking to increase customer engagement, boost retention, or maximize revenue, Teradata’s platform provides the tools you need to achieve your goals. If you need to use multiple technologies within your use case, our open and connected platform seamlessly integrates with other tools to ensure you have the best possible solution.

Seeing is believing: Try a hands-on AI/ML demo with ClearScape Analytics 

Test-drive ClearScape Analytics for free through a hands-on AI/ML demo that demonstrates the power of Teradata’s platform. Experience firsthand how unlocking hidden insights can transform your business. 

Leverage Teradata to maximize the potential of your data—and transform those hidden insights into actionable tactics and strategies that drive growth.

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Über Steve Anderson

Steve Anderson leverages over 20 years of extensive expertise in data science to shape and execute Teradata’s artificial intelligence marketing strategy. As a data practitioner, Steve has delivered innovative and impactful advanced analytics solutions across major industries, including financial services, retail, sports, and more.  Zeige alle Beiträge von Steve Anderson

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