Whether focused on a Single Customer View, Fraud Detection, Anti-Money Laundering, or supporting new data science initiatives with AI and Machine Learning, large-scale entity resolution or data matching is critical to ensure that you get accurate, trusted results and insights. These use cases typically require massive volumes of disparate data with multiple comparisons of the same data sets.
The ability of Trillium Quality to apply multiple algorithms including fuzzy matching across multiple fields with consistent scoring for different data segments is critical to achieving precise results. Further, the scalability offered in Trillium DQ for Big Data, delivers high-value data matching faster to help meet critical Service Level Agreements and meet data quality requirements when and where needed.
Read how Trillium DQ for Big Data helps you exploit unparalleled scale to deliver precise, accurate entity resolution for your critical use cases.