Built on the World’s Leading CDP, Segment Data Lakes Provides Businesses with the Foundation Needed for Advanced Analytics, AI and Machine Learning
Segment, the world’s leading customer data platform (CDP), announced the launch of Data Lakes, a new data architecture product built specifically to help companies create cutting-edge customer experiences with their customer data. Flexible, affordable and easy to use, Segment Data Lakes provides companies with the foundation needed to produce advanced analytics, uncover rich customer insights, and power machine learning and AI initiatives.
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“We all know that customer data creates a competitive advantage, but what companies sometimes fail to recognize is that the quality of their data architecture will determine just how significant that advantage really is,” said Tido Carriero, Chief Product Development Officer at Segment. “Segment Data Lakes gives data scientists the foundation they need to unlock the full potential of their customer data, so they can build better products and provide world-class customer experiences.”
When Data Warehouses Are Not Enough
Segment Data Lakes builds on the foundation that customer-centric, digitally-driven companies first created through data warehouses. For years, data warehouses have been a critical part of any company on the journey to digital transformation, because they provided access to key data needed to understand the digital customer journey. However, as the amount of customer data being generated continues to grow, and as customer expectations for highly personalized, real-time experiences increase, their limitations are now clear. Though they are valuable within a straightforward data architecture, data warehouses can limit a rapidly growing company’s ability to get the most from its customer data.
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Data warehouses are not designed with the flexibility that data scientists need to power complex machine learning and AI use cases — they often face severe limitations and are limited to SQL-only tools for analysis. In addition, performance issues and maintenance headaches are a common challenge. As a company’s data volumes multiply, the associated cost of storage only adds to the burden of keeping data warehouses running.
Uncovering custom insights and fueling predictive models also requires access to raw customer data, often going back years, as well as detailed granularity at the event level. This is not something that data warehouses are designed to hold, leaving data scientists without the historic data sets they need to build and train their models.
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