Trumid Chooses AtScale Semantic Layer to Drive Advanced Analytics and Data Science Initiatives

Trumid Chooses AtScale Semantic Layer to Drive Advanced Analytics and Data Science Initiatives

AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced that Trumid, a financial technology company and fixed income electronic trading platform, chose AtScale to build out its internal advanced analytics and data science capabilities.

Trumid centralizes performance data from multiple sources into a Google BigQuery data warehouse. AtScale connects BigQuery data to Google Looker reports, ensuring consistently high analytics performance and enabling permissioned non-technical users to interact with a business-oriented view of data. “Headline KPIs” related to aggregated volume and trading on the platform can be reviewed, and internal permissioned users are allowed to interactively explore more granular performance analytics. This approach enables a broader set of users to make data-driven decisions and unlocks the power of Trumid’s modern data and analytics stack.

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“Using AtScale in conjunction with Google Looker creates a powerful platform to capture valuable insights from our performance data”

“Using AtScale in conjunction with Google Looker creates a powerful platform to capture valuable insights from our performance data,” said Mutisya Ndunda, Head of Data Strategy and AI at Trumid. “By delivering a business-oriented view of data to a broader audience, we are able to drive more value from our cloud data and analytics investments.”

Trumid is also exploring the use of AtScale AI-Link to bridge data science programs to broader data and analytics users. This approach allows data scientists to treat the AtScale semantic layer as a feature store, simplifying access to business-vetted features for artificial intelligence (AI) and machine learning (ML) models. Further, model-generated insights can be published back through the semantic layer, enabling permitted business audiences to see a broader visibility of predictions.

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