By Thomas Kohlenbach – Data mining is a bit of a misnomer, because it is not the data that businesses are interested in digging up, but the connections, patterns and conclusions that it contains.

Data mining uses the power of cutting-edge computing to analyze data sets, employing artificial intelligence (AI) tools to identify patterns hidden within massive data sets and make projections based on them. It utilizes machine learning technology to reveal otherwise unidentified groupings in the information and use these as a basis to extrapolate predictions.

While data mining is a valuable tool in and of itself, it is the application of this technology to enterprise processes that shows just how effective contemporary computing techniques can be for business analysis.

Process mining is a subset of data mining that utilizes the information held in business systems and transactional computer records to create logs that, under digital analysis, reveal patterns of process execution and efficiency.

Process mining is the automation of what we have always known is essential: understanding your current processes and identifying the breakdowns.

What process mining allows us to do is leapfrog over the manual reviewing of data flows and procedural content, and dive straight into the heart of process excellence and continuous improvement.

Where it might take weeks, even months, of meetings and reviews to pin down where the inefficiencies in a process lie when using traditional methods, process mining can gather information about the flow of data or resources and identify choke points or breakdowns in a matter of hours. Additionally, the data is current and based on real-time statistics, not six-month-old anecdotal information.

With those kinds of insights in hand, business teams can immediately look to improve their processes with pinpoint precision, focusing efforts on what the data, rather than hearsay, has identified as the most significant holdups. Read On: