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Businesses live by their processesthe prescribed sets of actions their employees take to get things done. When processes run well, the business runs well. When processes run poorly, the business risks a host of hazards, from loss of revenue to customer dissatisfaction to compliance violations. Most businesses have a general idea of how their processes should run, but lack insight into the day-to-day details of execution. Without this insight, how can they make improvements that yield real results?

Process mining offers one solution, and for many years it served businesses well. However, in todays increasingly complex environment and amid growing pressure to do morefaster and at lower costsorganizations need more intelligent solutions.

In this article, well explore what process mining is, what it can (and cant) do for businesses looking to optimize their processes, and how Process Intelligence offers a more effective approach.

What is process mining

Process mining uses actual data from information systems to create a model that accurately reflects how a process executes.

Applications such as CRM and ERP systems, as well as other systems of record, automatically create event logs that record every action taken. The data in these logs can be collected, or mined, to create an audit trail of the processes the applications are involved in. This works even when multiple applications are used in a single process. Process mining technology follows these audit trails to build a process model showing the details of the end-to-end process, as well as variations. Business users can analyze these models to find out if the processes are functioning as they should and, if not, investigate the root causes of deviations from the optimal path.

How process mining works

Before process mining, the only way for businesses to analyze the performance of their processes was through interviews with business users and manual data reviewsa slow, tedious undertaking with a high margin of error. Process mining allows organizations to leverage automation to paint accurate pictures of real-world process performancefaster, easier, and more accurately than manual approaches.

Where process mining falls short

Process mining offers enormous advantages over manual approaches to process analysis, but it has its limitations. For example:

How Process Intelligence bridges the gap

A new generation of process analytics solutions goes beyond traditional process mining.Process Intelligencecombines BI-like metrics with a set of process-specific analytics to offer detailed insights into complex processes from end to end. Unlike traditional process mining, Process Intelligence enables businesses to view their processes in real time and analyze patterns that lead to bottlenecks or disruptions.

Here are the top four advantages Process Intelligence offers over traditional process mining:

1. Timeline-based analysis

Process mining uses the schema method of process analysis, which involves converting process data into a flowchart (schema) and then analyzing the flow of all iterations through that schema. The shortcoming of this approach is that few business processes fit into a well organized flowchart. By the time all valid variations to a process are considered, the schema often becomes a tangled mess with limited usefulness.

By contrast, Process Intelligence uses atimeline approach, which creates an unfiltered, unedited history of every process iteration from beginning to end. These timelines are then analyzed so that they can be compared, filtered, searched, aggregated, etc., similarly to how a BI application analyzes records in a table. The timeline approach offers complete visibility into the process from end to end, even when some steps are performed using multiple systems. And the numerical analysis approach of Process Intelligence, as compared to the schema-centric approach of process mining, works equally well on all types of processes, as compared to basic process mining that only works well on processes with little variability in terms of the sequence of steps.

2. Continuous improvement

Traditional process mining is focused on looking at historical data. While this approach can offer valuable insights into what worked well and what didn't, it falls short of offering solutions for present and future iterations.

Process Intelligencemonitors processeswith new data coming inreal and near-real time, watching every iteration and alerting process owners for deviations that could cause delays or other problems. By enabling continuous improvement, Process Intelligence continues to deliver ROI as businesses capitalize on new opportunities to make processes work faster and smarter.

3. Reduces compliance risks

When businesses run traditional process mining applications, users can review the output to identify present and past deviations that could lead to compliance issues. This approach relies on the expertise of the users reviewing the data.

Process Intelligence enables users to define process rules that align with the organizations compliance requirements and to instruct the system to watch for violations. When one or more of those rules is broken, the system alerts users right away, enabling them to take immediate action to rectify the deviation and to ensure that it will not happen again. Process Intelligencealerting rulescan also be defined to call a service when an alert is triggered, to automatically deal with the problem. This capability can mean the difference between discovering an issue just in time, before it affects a business compliance status, and finding it when it is too late to be fixed and has already caused problems elsewhere in the workflowor worse, learning about it after a violation has been reported.

4. RPA enhancements

According to Ernst & Young, between30 and 50 percent of initial robotic process automation (RPA) projects faildue to lack of quantifiable process data. As businesses deploy RPA for more intricate processes in more complex environments, the pressure to deliver positive ROI has increased dramatically, and traditional process mining can offer only limited support in yielding the returns that businesses are seeking.

Fortunately, Process Intelligence can be just as valuable to digital workers (RPA "bots") as it is to human employees. Todays Process Intelligence solutions can includeprocess mining and task mining. As with process mining, task mining is looking for the significant events in a process. Task mining adds the ability to record a users manual actions on their computer to capture manual process steps to be used alongside the steps gleaned from system of record log files. By applying Process Intelligence to manual as well as automated processes, businesses uncover new opportunities to improve RPA results:

Why Process Intelligence is the future of process improvement

For many years, process mining applications served process owners well, saving countless manual hours and helping businesses discover opportunities for improvement. Process Intelligence provides a new approach to process improvement that improves on process mining. Process Intelligence works with all processes, simple and highly variable, manual and automated. Process Intelligence will monitor every process instance as each new step occurs, alerting or even taking automated action whenever a process behavior of interest is seen.

Process Intelligence supports RPA initiatives by identifying good automation candidates and then monitoring and reporting on the process that bots participate in. Process improvement can now reach a new level in delivering on its promises of greater productivity, reduced risk of costly compliance violations, and the streamlined efficiency that can create happier customers, happier employees, and a greater competitive edge.

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What Is Process Mining (and Why Isn't It Enough)? - IDM.net.au