  "Bad" data has a direct impact on 88% of companies, with the average company
losing 12% of its revenue due to it. Duplicates - multiple but different
representations of the same real-world entities - are among the main reasons
for poor data quality, so finding and configuring the right deduplication
solution is essential. Existing data matching benchmarks focus on the quality
of matching results and neglect other important factors, such as business
requirements. Additionally, they often do not support the exploration of data
matching results. To address this gap between the mere counting of record pairs
vs. a comprehensive means to evaluate data matching solutions, we present the
Frost platform. It combines existing benchmarks, established quality metrics,
cost and effort metrics, and exploration techniques, making it the first
platform to allow systematic exploration to understand matching results. Frost
is implemented and published in the open-source application Snowman, which
includes the visual exploration of matching results.
