Organizations become more data-intensive and companies try to reap the benefits from this. Although there is a large amount of data available, this data has often different qualities which hinders use. Creating value from big data requires dealing with the variations in quality. Depending on their quality, data need to be processed in various ways to prepare this data for use. Although the processes vary, dealing with certain levels of data quality is a recurring challenge for many organizations. By developing generic process patterns organizations can reuse each other solutions. In this paper, process patterns for dealing with various levels of data quality are derived based on a case study of a large telecom company that employs all kinds of data to create operational value. The process patterns can possibly be used by other organizations.