The term Big Data’ has been hyped in the recent years and data mining techniques are now being used in many areas. This leads to a growing demand for data scientists, who can turn data into value. Data science aims to use the different available data sources to answer four key questions:
- What happened? -> Report
- Why did it happen? -> Diagnose
- What will happen? -> Predict
- What is the best that can happen? -> Recommend
These four questions are key to understand and improve business processes. Data mining techniques applied to business process analysis (aka process mining) aims to discover, monitor and improve real processes by extracting knowledge from data readily available in today’s information systems. The goal is not to analyze data, but to improve processes for your business.
Starting point for process mining is an event log. Each event in such a log refers to an activity (i.e., a well-defined step in some process) and is related to a particular case (i.e., a process instance). The events belonging to a case are ordered and can be seen as one run’ of the process. Event logs may store additional information about events such as the resource (i.e. person or device) executing or initiating the activity, the timestamp of the event, or data elements recorded with the event (e.g. unit cost).
Process mining bridges the gap between traditional model-based process analysis (e.g. simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining compares event data (i.e. observed behavior) and process models (hand-made or discovered automatically).
There are three main types of process mining [see (Ref. 1)]:
- Discovery: A discovery technique takes an event log and produces a process model without using any a-priori information.
- Conformance: An existing process model is compared with an event log of the same process.
- Enhancement: The objective is to extend or improve an existing process model using information about the actual process recorded in the event log, for example the extension of a process model with performance information, e.g., showing bottlenecks.
Finally yet importantly, process mining techniques can be used in an online setting known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place.
These relatively new techniques are applied in several business sectors, private or public, and deliver valuable insights where businesses are under pressure for effectiveness and quality [e.g. Healthcare sector, see (Ref. 2)]
Process mining is a new way of looking at your business processes!
(1) Wil M.P. van der Aalst, Process Mining : Discovery, Conformance and Enhancement of Business Processes, ISBN 978-3-642-19344-6, Springer-Verlag, 2011
(2) Ronny S. Mans Wil M.P. van der Aalst, Rob J.B. Vanwersch Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes, ISSN 2197-9618, SpringerBriefs in Business Process Management, 2015
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