Data Collection for Process Intelligence

Data Collection is the fundamental requirement of process intelligence in a business organization, as it allows the organizations to analyze and optimize their processes and workflows.

Data that is systematically collected and processed can help business organizations to gain actionable insights into their day-to-day operations, identify issues and inefficiencies, and provides suggestions to make informed decisions.

Read this chapter to learn the process and methods of data collection in the context of process intelligence along with key challenges that organizations face while collecting their process data.

What is Data Collection in Process Intelligence?

In the context of process intelligence, data collection is a process of gathering data and information related to business processes and workflows from various tools and systems. This process data is then utilized to understand the workflows and improve them.

Effective data collection in Process Intelligence is crucial for mapping and analyzing processes accurately, optimizing workflows, and making predictions about future challenges.

Methods of Data Collection in Process Intelligence

Process intelligence employes various automated data collection methods to gather business process data within an organization.

Some of the common methods are explained below −

• Event Logs and System Traces − This method collects data generated by enterprise applications like ERP, CRM, and BPM. It provides a record of every user and systems interaction.
• Sensor Data − This method collects operational data from IoT (Internet of Things) devices installed across the organization to continuously monitor the processes and workflows.
• Workflow Monitoring Tools − Process intelligence also uses various process mining tools like UiPath, Celonis, etc. to collect and organize process data automatically.

Challenges in Data Collection for Process Intelligence

Data collection is an important aspect in process intelligence, but it also has certain challenges associated with it, which are described here −

• Data Quality Issues − The automatically collected process data may be incomplete or inconsistent and can affect the process analysis and optimization activities.
• Relevance of Data − It is also challenging to identify which process-related data is important, and it becomes more challenging in large organizations with complex processes and workflows.
• Big Data Complexity − As the process intelligence involves the collection of structured and unstructured data collectively. It makes the collection, analysis, and processing of this data more complicated.

Steps Involved in Data Collection for Process Intelligence

For effective implementation of process intelligence in any business organization, it is important to build a robust framework for data collection. The steps given below can be followed to achieve this −

Step 1 − First of all, we have to define a clear objective for data collection.

Step 2 − Next, we have to determine the type of data required for implementing process intelligence.

Step 3 − Then, we will identify the sources that can provide relevant data for process intelligence.

Step 4 − After that we will select the most appropriate tools and methods for collecting process data from various points across the organization.

Step 5 − Finally, the data collection process will initiate with ensured data privacy and compliance standards, and the collected data will be transformed to make it suitable for analysis and process intelligence.

Best Practices for Data Collection in Process Intelligence

Business organizations can adhere to the following best practices to ensure effective data collection for process intelligence −

• Organizations should use automated process mining tools and monitoring software to collect accurate process data.
• They should regularly audit automatically collected data to ensure accuracy, relevance, and maintain integrity.
• They should follow regulatory standards to ensure data privacy and avoid any legal complications.
• Organizations should enable collaboration among various departments to reduce data fragmentation issues.

Conclusion

In process intelligence, data collection is an essential process that allows organizations to collect both structured and unstructured data about their workflows and processes to determine inefficiencies, growth opportunities, and make decisions to streamline business operations.

In this chapter, we explained all the important aspects related to data collection in process intelligence. Let's move ahead to the next chapter and learn about the tools used to implement process intelligence.