Conference paper abstractA significant amount of operation and maintenance (O&M) data are being generated daily from offshore wind farms. Most of them are coming from a variety of monitoring systems, maintenance reports and environmental sources. The challenge with having a wide diversity of data in inhomogeneous types and formats, is the considerable human effort involved in the initial extraction, transformation and loading (ETL) stages for these data to be processed and analysed. Although several commercial solutions are available, aiming to improve data management to support O&M decision making, the initial ETL phase is still a work-intensive process. One of the main reasons is that the organization and structure of the data flow does not allow easy access to the data. Due to the rapid growth of the offshore wind industry, there is a need to automate and integrate some of these processes in order to reduce the human effort and the associated costs. The aim is to facilitate a responsive, data driven decision making for O&M. This paper and presentation show the results of re-structuring and automation of the daily maintenance procedures that achieve a more efficient data analysis. These early results also indicate that less man-hours and a smaller number of people need to work on data collection. The framework and the steps followed will be of interest to offshore wind farm developers and operators to automate their data collection workflow
