Automated business process discovery (ABPD) as a complementary approach overcomes many of these shortcomings to create a business process model at a fraction of the time and cost involved in the traditional way. One major benefit of ABPD is process discovery across the “white space,” the process knowledge gap that exists between departments and functions and at the edges of processes. Modeling can be done by hand, so there may be no cash-flow impact, unlike ABPD, where a tool is necessary. By observing the details of how processes are executed in the supporting technology, ABPD uses unstructured, event-level data to automatically build process definitions and models, and explore process variations. Because of the quantitative nature of this data, the derived process models enable rich and interactive analysis. ABPD techniques start from event logs (audit trails, messages, transactions, databases and so forth), and try to discover patterns to fit a process model to the information obtained from the events. The underlying techniques are strong enough so that users don’t have to specify a process model; it is created from scratch by pattern recognition. Moreover, ABPD delivers information on bottlenecks, variances, root causes and the distribution of the process instances, thus enabling meaningful simulation. It’s all about capturing what has happened in a real-life process. ABPD is a form of process optimization. Of course, ABPD does not capture informal human tasks that are not automated and represented as events in an audit trail or transaction log. This is why ABPD is best combined with techniques from social BPM and BPA for the masses. However, ABPD accelerates process discovery and identifies previously unseen process patterns. Planners must still conduct interviews to capture informal work practices, but organizations no longer need to spend as much time using interviews to discover the implicit processes previously hidden in automated solutions.