Navigating Common Challenges in Process Mining with Qlik Sense

Process mining is being applied to give an understanding of the ways business processes are conducted by an organization on its way to operational excellence and efficiency. Process mining, embedded in a data-driven way of extracting knowledge from event logs, brings valuable insights into process execution and deviations.

Qlik Sense, the most flexible self-service business intelligence platform, puts the power of process mining in the hands of organizations that are ready to drive continuous improvement initiatives. However, the process mining tool for Qilk Sense also features some challenges, like any transformational exercise.

In this blog, we’ll explore some of the challenges companies present in process mining using Qlik Sense and how to overcome them:

Data Quality and Availability

A primary challenge of Qlik Sense process mining is ensuring good quality and data availability. Event logs from IT systems are usually incomplete, inconsistent, or even fragmented, making it difficult to understand the process. Organizations need to consider investing in various data-cleansing and enrichment techniques that include data profiling and normalization of data towards enhancing the quality, integrity, and reliability of event data. Complexity of Process Mapping :

Another challenge in process mining with Qlik Sense is correctly mapping business processes. Most business organizations struggle because of the enormous complexity of their processes, having many stakeholders, several systems, and many points for making decisions. This could be tackled if organizations take a phased approach to process mapping, starting with high-level process flows that will be further detailed in AFM steps. With Qlik Sense’s intelligent drag-and-drop interface, process maps can interactively be created for the visualization of businesses and to identify hitch points that need to be optimized.

Performance and Scalability

As organizations scale their process mining initiatives, they may start hitting at issues related to the performance or even the scalability of Qlik Sense. Slicing through large volumes of event data in real time may strain system resources and slow down the performance of such systems. Addressing these problems will require optimizing the data model and implementing data caching strategies to enhance query performance.

Besides, the Qlik Sense associative data model enables the distribution of organizational workloads over several nodes, thus offering better scalability and the ability to handle the growing data capacity. Lack of Stakeholder Engagement:

Engaging stakeholders in practical ways to implement process mining initiatives using Qlik Sense is essential. Organizations may face resistance or apathy from relevant stakeholders, who view process mining as disturbing or intrusive. One can get out of such situations by building the value proposition around process mining and explaining the potential of process mining for driving operational improvements, cost savings, and competitive advantage.

With the engagement of stakeholders at an early stage of the process mining journey and obtaining input and feedback from them, the firm would, therefore, grow a data-driven culture and collaborative decision-making.

Process mining in Qlik Sense reflects the great potential for businesses in advanced analytics and the usability of user interfaces in driving improvement in their business processes. It will enable organizations to proactively manage those challenges and elevate process mining to a strategic imperative in deriving new insights, pointing out optimization opportunities, and making a difference in delivering tangible business outcomes. As organizations journey toward process mining with Qlik Sense, the door opens to more efficiency, agility, and competitiveness in ever-growing, dynamic business environments.