David Menninger's Analyst Perspectives

Process Mining: Improve Execution and Operations with Analytics

Posted by David Menninger on Apr 12, 2021 3:00:00 AM

VR_2021_Process_Mining_Assertion_1_Square (2)Process-mining software isn’t exactly new, but it’s also not widely known in the software technology market. The discipline has been around for at least a decade, but is generating more interest these days with both specialist vendors and major enterprise software vendors offering process-mining products and services. We assert that through 2022, 1 in 4 organizations will look to streamline their operations by exploring process mining.

Process mining uses information gathered from enterprise software applications to understand and improve an organization’s operational processes. Log data provides an abundance of information about what operations are occurring, the sequences involved in the process, how long the processes are taking and whether or not the process was completed successfully. As computing power has increased and storage costs have decreased, the economics of collecting and analyzing large amounts of log data have become much more attractive.

Understanding and analyzing operational processes in such detail can provide significant benefits. If an organization can identify processes that are bogged down and improve them, it can reduce the resources required for those processes. For example, an analysis of the accounts receivable process could uncover that 20% of receivables require extended collection periods because the amounts billed to customers don’t match the purchase orders. If those mismatches can be identified and prevented, there will be less time and cost needed to follow up on the outstanding receivables. In addition, the money can be collected sooner, which also improves the bottom line. Once the resolutions to these business process exceptions have been identified, the next step would be to explore options to automate the solution, further increasing efficiency. Or, the resolution may identify an issue in an upstream system that can be fixed, preventing similar issues from occurring in the future. Even if process mining merely identifies an issue sooner, it begins the resolution process sooner.

There are challenges to collecting all of the log data from various applications in an organization. Widely used applications provided by third-party software vendors often include well-documented log files or application programming interfaces, making it easier to access this information. However, many organizations use custom applications which may not be accessed as easily. It may be difficult to capture information from customized third-party applications as well. And, organizations often use applications from a variety of vendors. Relating the information about processes that span multiple applications also presents challenges.

Understanding these challenges, process-mining platforms should include:

  • Pre-built log file collectors and application connectors to make it easier to access all relevant information sources. Regardless of how many applications are supported out of the box, it’s likely that organizations will need to implement custom connectors as well, so the platform should include a toolkit for additional connectors.
  • A framework for analyzing the processes that includes sophisticated artificial intelligence and machine learning capabilities to identify subtle relationships between steps in the process and across processes.
  • A recommendation engine to help determine likely resolutions to the issues identified as well as track and analyze those resolutions to understand which were most effective.
  • An automation framework for resolutions that includes connectors back to the underlying business applications to enable organizations to close the loop and minimize resources needed to implement the resolutions.
  • The capability to report and visualize information about the processes and the resolutions.

Organizations, particularly those using packaged software applications and services, should consider process mining if they haven’t already. Multiple vendors are offering these capabilities, including perhaps your application vendor. If you have a multi-vendor application portfolio, make sure you can support the collection of applications in your organization and consider whether additional data collection capabilities are necessary based on your application portfolio. The bottom line is that process mining can help improve the efficiency of your operational processes, and we recommend you explore how it might benefit your organization.

Regards,

David Menninger

Topics: business intelligence, Analytics, Digital Technology, AI and Machine Learning, robotic automation

David Menninger

Written by David Menninger

David is responsible for the overall research direction of data, information and analytics technologies at Ventana Research covering major areas including Analytics, Big Data, Business Intelligence and Information Management along with the additional specific research categories including Information Applications, IT Performance Management, Location Intelligence, Operational Intelligence and IoT, and Data Science. David is also responsible for examining the role of cloud computing, collaboration and mobile technologies as they affect these areas. David brings to Ventana Research over twenty-five years of experience, through which he has marketed and brought to market some of the leading edge technologies for helping organizations analyze data to support a range of action-taking and decision-making processes. Prior to joining Ventana Research, David was the Head of Business Development & Strategy at Pivotal a division of EMC, VP of Marketing and Product Management at Vertica Systems, VP of Marketing and Product Management at Oracle, Applix, InforSense and IRI Software. David earned his MS in Business from Bentley University and a BS in Economics from University of Pennsylvania.