Forest resource data is important in targeting the forestry operations, and it is in the hearth of the precision forestry concept. The forest resource data can be produced with many techniques, and the number of existing forest data sources has increased during the years. In addition to the forest resource data, other data describing the circumstances of the forest site, such as trafficability and weather conditions, are available. In Finland, a forest data platform gathers the data sources under a single service for easier implementation of the precision forestry applications. This data is useful in operations planning, but it also describes the conditions that prevail when the forest machine arrives to the forest site. This study proposes data fusion between fieldbus time series of the forest machine and the forest data. The fused dataset enables explorative statistical analysis for examining the relationship between the machine performance and the forest attributes and provides data for building predictive models between the two. The presented methods are applied into a dataset generated from a field test data. The results show that some fieldbus time series features are predictable from forest attributes with R2 value over 0.80, and clustering methods help in interpreting the machine behavior in different environments. In addition, an idea for generating a new forest data source to the forest data platform based on the fusion is discussed.