Tech manufacturer cuts compute costs by 40%

40% Reduction in compute consumption
Faster Data preparation
Lower Total cost of ownership

はじめに

概要

Pricing at scale demands analytics at speed

A Fortune 50 global technology manufacturer competes across tens of thousands of configurable products in markets worldwide. To price competitively at that scale, the company relies on sophisticated predictive models driven by massive volumes of transactional, product, and market data. The intelligence was in place. The pipeline was the problem.

チャレンジ

Data movement slowed it all down

The company's price recommendation model was held back not by the science, but by the engineering around it. Data had to be extracted from the enterprise data warehouse, moved externally, and processed through lengthy, compute-intensive jobs—including imputation, outlier removal, and feature engineering—all running outside the platform where the data actually lived. The result was bottlenecks, delayed model refresh cycles, inflated infrastructure costs, and a data science team that couldn't iterate fast enough to keep pace with the business.

An aerial view of a warehouse with workers, forklifts, and stacked pallets

ソリューション

Bringing computation to the data

Teradata moved the entire preparation workflow in database, running it directly where the data resides. Missing value imputation, outlier detection, and feature engineering all execute natively, exploiting Teradata's massively parallel architecture. No extraction. No external processing. No waiting. Teradata then went further, using the Teradata Enterprise Feature Store to give the team the ability to persist, version, and reuse engineered features. This ensures complete consistency between training and scoring and dramatically accelerates future model development.

結果

From constrained to confident

The impact was immediate and measurable. Compute consumption dropped by 40%. End-to-end data preparation time was significantly shortened, and total cost of ownership came down. The data science team gained a reusable, governed feature engineering framework that accelerates delivery of predictive models—not just for pricing, but across the entire analytics estate. The company is now scaling AI where it matters most.