Case Study: Deluxe achieves real-time, data-driven machine learning and breaks down data silos with IBM StreamSets

Streaming Data Platform for Machine Learning

Deluxe, a century-old company that now serves 4.8 million small business customers and over 4,600 financial institutions, needed real-time data and modern analytics to support advanced machine learning and personalized marketing. With 300+ disparate data sources, legacy ETL could not keep up—data availability was slow, change data capture was lacking, and answering customer or data-product requests could take days—so Deluxe brought in IBM StreamSets and its StreamSets Data Collector (deployed on Microsoft Azure) to address the gap.

IBM StreamSets implemented streaming data pipelines, automated ingestion and change data capture, and in‑flight data cleaning and augmentation to centralize 300+ sources into Deluxe’s Azure data platform. The StreamSets Data Collector solution enabled near real-time data availability, supported the creation of advanced ML models and real-time marketing products, and helped break down legacy silos—moving Deluxe from multi-day turnaround to delivering fresher data and actionable insights.

Deluxe

Jean-Marie Bertoncelli

Big Data and Analytics Architect