Process Intelligence - Quick Guide

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Process Intelligence - Introduction

Process Intelligence (PI) is an approach to collect, analyze, and synthesize process data using artificial intelligence-driven technologies. It is basically a crucial element of process discovery in business organizations. Process intelligence is mainly used to transform raw data of processes and workflows of a business organization into accurate process insights. This helps businesses to identify inefficiencies and issues in workflows, and allows to automate and optimize their processes across the organization.

Process Intelligence is considered an advanced concept that integrates multiple modern technologies like artificial intelligence (AI), machine learning (ML), advanced data analytics, and many other to provide actionable insights and optimize business processes.

In this chapter, we will learn about the basics of process intelligence and its significance in business process automation and optimization.

What is Process Intelligence (PI)?

Process intelligence is an advanced concept that allows business organization to integrate the best aspects of task mining, process mining, and business intelligence to identify and adjust their processes and workflows. It allows organization to scale their process automation, identify inefficiencies, and maximize the process performance.

Hence, the primary objective of process intelligence is to provide a business organization with more accurate information about what processes and workflows exist and what issues are associated with them.

How Does Process Intelligence Work?

As we know any business organization has a number of processes and workflows. But most of the organizations do not know how their processes are actually executing and what people are engaged in them and how they are interacting with process data. Also, the business processes change over time, and inefficiencies and redundancies are introduced in them. When it comes to a large scale, this fragmented process system affects the overall productivity of the organization.

However, process intelligence makes use of advanced technologies like artificial intelligence, machine learning, natural language processing, business process automation, computer vision, etc. to continuously monitor and interpret the process performance across all the departments of the organization. This provides a real-time comprehensive digital map of processes of the organization and helps identifying and resolving issues and bottlenecks.

The work flow of process intelligence in a business organization involves the following key activities −

Data Collection − First of all, process data is collected from all systems and applications in the organization.
Aggregation − Collected process data is then compiled.
Cleaning − The unstructured complied and aggregated process data is transformed into structured information.
Analysis − The process data is then interpreted and converted into reports and visualizations.
Take Action and Automate − Insights obtained from process data analysis are used to make decisions and automate processes.

How to Implement Process Intelligence?

The key steps involved in implementing process intelligence in a business organization are explained here −

Step 1 − Firstly, define the areas where process intelligence can be used or established within the business organization.
Step 2 − Select appropriate process intelligence tools that come embedded within an intelligent automation platform and work seamlessly with existing processes.
Step 3 − Put the process intelligence tools in execution and observe how they perform operations in real-time.
Step 4 − Utilize observation data to identify inefficiencies and improve by redesigning processes and optimizing automation.

Why Process Intelligence is Important?

As we discussed above, process intelligence helps business organizations to discover, automate, analyze, and optimize their processes in an effective and faster way. It allows to ensure that the processes meet the business goals and work at their maximized performance. Hence, we can say process intelligence plays an important role in delivering better customer experiences and employee satisfaction in the organizations.

Some of the key factors further highlighting the importance of process intelligence in business organizations are as follows −

It allows to identify major inefficiencies and issues in processes and workflows, hence provides enhanced operational efficiencies.
It uses process data to provide actionable insights.
It improves the processes by eliminating issues like delays and errors.
It also enhances customer interactions and satisfaction.
PI allows organizations to quickly adapt to changes in technologies, market trends, and regulations.
By allowing to adhere to compliance standards, PI minimizes the risks of regulatory violations.

Conclusion

In this chapter, we explained some of the introductory concepts of process intelligence (PI). From the above discussion, we can conclude that process intelligence is a modern approach used by business organizations to assess their operational performance, identify bottlenecks in their processes, and optimize their workflows through automation.

Let's move to the next chapter and understand how Process Intelligence is different from the Process Mining.

Process Intelligence vs Process Mining

This chapter provides a comparative study of two very important concepts of business process automation namely, process mining and process intelligence. Although these two concepts seem similar at first glance, both are different from each other and the most significant difference between them lies in their scope and core functionality.

Process Mining is a concept of discovering, analyzing, and visualizing business processes as they are actually executed within a business organization. In contrast, Process Intelligence is an advanced approach for optimizing processes and making them more intelligent.

Let's discuss the other important differences between process mining and process intelligence.

Difference between Process Mining and Process Intelligence

The following table highlights all the major differences between process mining and process intelligence −

Parameter	Process Mining	Process Intelligence
Definition	Process mining can be defined as a data-driven technique used to understand how processes are really executed.	Process intelligence is defined as a holistic approach that combines contextual information with advanced analytics and automation to provide actionable insights and optimize processes.
Objective	Process mining is a concept meant for visualizing processes, identifying issues, and inefficiencies in existing operations.	Process intelligence is an advanced approach that not only analyzes the processes but also optimizes and makes them intelligent by predicting issues and managing them in advance.
Primary Function	Process mining provides a clear screenshot of actual processes by analyzing event log data.	Process intelligence optimizes processes and enhances decision making.
Outcome	Process mining provides performance metrics and workflow maps that highlight the actual state of processes within the organization.	Process intelligence forecasts potential challenges in processes and suggests areas for improvement.
Tools Used	Process mining uses data extraction tools to extract data from event logs and visualization tools to generate process maps and highlight inefficiencies.	Process intelligence uses artificial intelligence, machine learning, predictive analytics, and contextual data to optimize processes and enhance decision-making.
Timeframe Analyzed	Process mining analyzes only historical data from event logs to understand past and current operations and process executions.	Process intelligence provides combined analysis of historical data, current process monitoring, and predictive insights for future challenges.
Primary Focus	Process mining is primarily focused on identifying and correcting existing bottlenecks and inefficiencies in workflows.	Process intelligence focuses on continuous optimization of processes and predicting future challenges.
Core Activities	Process mining involves activities like process discovery, visualization, and process enhancement.	The key activities involved in process intelligence are real-time monitoring, predictive analysis, and proactive optimization of processes.
Frequency of Use	Process mining is used depending on the needs to audit and specific process enhancement.	Process intelligence is used continuously and embedded in daily business processes.

Conclusion

In this chapter, we highlighted all the key differences between Process Intelligence and Process Mining. The most fundamental difference is that process mining provides a view into the current execution of a specific process within a business organization and is used depending on the needs to audit processes. Process Intelligence, in contrast, provides a comprehensive solution to identify issues and optimize the processes and is used continuously and embedded in daily business operations.

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.

Data Collection for Process Intelligence

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.

Process Intelligence - Tools

In todays world of data and technological advancement, business organizations constantly require techniques and tools to enhance operational efficiencies in their processes, improve customer experiences, and achieve sustainability in the business. Process intelligence tools play an important in achieving all these goals, as they allow business organizations to deeply analyze and understand their processes and workflows to identify inefficiencies and make informed decisions to optimize them.

This chapter is meant for explaining the functions and importance of various process intelligence tools. The main function of process intelligence tools is to use data to determine how business processes in an organization run and how they can be optimized.

What is a Process Intelligence Tool?

Process intelligence tools are advanced technologies, software, and methodologies designed and used to monitor, analyze, and optimize the business processes. These tools collect data from various sources like ERP, CRM, and workflow monitoring systems and process them to provide actionable insights to optimize workflows in the organization.

Process intelligence tools allow business organizations to perform the following key activities −

Identify inefficiencies and problems in processes and workflows
Measure performance of the processes
Determine areas of automation
Provides insights to enhance productivity
Predict future trends and outcomes, etc.

Main Functions of Process Intelligence Tools

Process intelligence tools typically perform the following key functions in a business organization −

Collects data from various sources
Creates visualizations and maps of processes and workflows
Analyzes the collected data to identify inefficiencies and areas that need optimization
Monitors the performance of workflows and processes in real-time

Let's now have a discussion on some key tools used for implementing process intelligence in business organizations.

Process Mining Tools

These are tools developed and utilized for analyzing event logs from various enterprise systems like EPR, CRM, etc. Process mining tools provide information about how processes are actually executed. In short, these tools provide a clear view of processes and workflows within the business organization.

The key functions that process mining tools perform include –

Data extraction from various systems like ERP, CRM, workflow monitoring systems, etc.
Creates visuals and maps of processes and workflows to identify inefficiencies
Highlights the key reasons of potential issues and risks

Hence, business organizations find process mining tools important because of the following reasons −

Helps organizations to deeply understand their workflows and processes.
Allows them to perform data-driven process improvements.

Workflow Automation Tools

As the name implies, workflow automation tools are those process intelligence tools that designed and used to automate repetitive tasks and streamline business operations. These tools help reducing manual, human intervention in the processes and workflows, and improve operational efficiencies.

The key functions that workflow automation tools perform in process intelligence are as follows −

Automates repetitive, routine tasks and processes within an organization
Provides data analytics about performance of workflows
Reduces human intervention in process executions

Therefore, workflow automation tools are also important parts of process intelligence due to the following reasons −

Allows organizations to eliminate human involvement and errors in processes.
Helps organizations to scale their operations and enhance the operational efficiency.
Frees up human resources for higher-value tasks and operations.

Business Process Management (BPM) Tools

Another important process intelligence tools are business process management (BPM) software. These tools are designed to systematically model, execute, monitor, and improve the processes of a business organization.

The main functions that BPM software perform as a process intelligence tool include −

Allows organizations to model processes with simple drag-and-drop capabilities
Provides a platform to manage business processes and workflows
Creates interactive dashboards for real-time monitoring of processes, etc.

BPM software marks their importance in process intelligence because of the following key reasons −

Allow organizations to control their end-to-end processes.
Help organization to ensure that workflows align with their business goals.
Ensure that processes meet the compliance requirements, etc.

Data Analytics Platforms

Data analytics platforms are also important tools used in process intelligence. These tools provide capabilities to analyze large volumes of process-related data of a business organization. They help gaining insights about process performance, identify patterns, and predict trends.

Some of the key functions that data analytics platforms perform as a process intelligence tool are as follows −

Collects and analyzes data from various sources within the organization.
Uses advanced algorithms and machine learning capabilities to identify inefficiencies and bottlenecks.
Allows to perform predictive analytics to optimize workflows and processes.

The following features of data analytics platforms make them important in process intelligence −

Provides capabilities of data-driven decision making
Helps organizations to predict potential issues
Allows protectively optimize the workflows and processes, etc.

Process Monitoring Tools

In process intelligence, process monitoring tools provide capabilities to monitor and track the real-time performance of processes and workflows. They provide alerts and help avoiding potential issues in process executions.

The following are key functions of process monitoring tools in process intelligence −

Performs real-time tracking of processes and workflows
Provides alerts for issues and deviations
Allows automation systems to automatically take corrective actions

The benefits making process monitoring tools important in process intelligence include −

Makes process executions smoother
Allows to meet regulatory standards in workflows
Reduces errors and breakdowns, etc.

Conclusion

Process intelligence tools are important parts for optimizing business processes and enhancing operational efficiency of business workflows. In this chapter, we have discussed the functions and importance of various process intelligence tools that businesses commonly employ in their organizations

Applications of Process Intelligence

This chapter is meant for explaining the key applications or use-cases of process intelligence. In today's world of data and technological advancements, business organizations feel constant pressure to speed up their production, optimize workflows, eliminate errors, minimize operational costs, and meet the targeted goals. Process intelligence allows these organizations to achieve all these expectations by combinedly using advanced technologies like process mining, artificial intelligence, and advanced data analytics.

Process Intelligence enables organizations to effectively manage and enhance their processes and workflows by providing a detailed view of processes. Let's discuss some use-cases that show how process intelligence allows business organizations to revolutionize their workflows and operations.

Process Intelligence Use Cases

Some of the key applications of process intelligence in business organizations are explained here −

Process Discovery

Process discovery is one the very common applications of process intelligence. Businesses use process intelligence to know how their processes are actually executing within the organization. It makes use of event log data to automatically generate maps of processes and workflows in real-time and allows organizations to identify inefficiencies and non-compliance.

Process intelligence provides an error-free and faster approach for process discovery which is completely data-driven, instead of based on assumptions or manual mapping.

Workflow Optimization

Process Intelligence also plays an important role in optimizing workflows and operations within a business organization. As we know, inefficiencies like operational issues, delays, or rework loops can significantly affect the productivity of a business organization.

Process intelligence provides a comprehensive view of process performance of the organization and highlights the inefficiencies. Using these insights, businesses can eliminate issues and streamline their workflows.

Compliance Monitoring

Process intelligence is also used for enabling continuous monitoring of regulatory standards and compliance, especially in regulated industries like manufacturing, finance, healthcare, etc. This continuous monitoring helps organizations to determine deviations from standards and ensure adherence to regulatory standards.

Process intelligence also enables organizations to perform a detailed audit and regulatory investigation, and provides ways to minimize risks of non-compliance.

Operational Risk Management

Process Intelligence is employed in organizations to detect errors and irregularities in workflows, and it provides ways to proactively manage the potential operational risks. This benefits the business organizations by preventing expensive workflow or process disruptions.

Cost Reduction

Business organizations also use process intelligence as an effective tool for reducing their costs and increasing profit margins. PI provides capabilities to identify and eliminate inefficiencies and unnecessary steps in business operations. This contributes in better resource utilization and significant cost savings.

Customer Experiences Optimization

Process intelligence is used to better understand customer's requirements and provide improved solutions. It also enhances interactions between business organization and customers. Hence, process intelligence enhances overall customer satisfaction and experiences.

Identifying Automation Opportunities

Process intelligence plays a critical role in identifying possible areas for automation within a business organization. As we know, a business organization has a large number processes, but not all the processes are suitable for automation, and choosing the right ones is a very difficult task. But process intelligence uses advanced analytics to identify those repetitive, low-value, and time-consuming processes which are suitable for automation using RPA and AI. Hence, process intelligence allows businesses to smartly automate their operations.

Resource Allocation

Business organizations also use process intelligence to understand how their resources are being utilized in processes and workflows. PI highlights which resources are underused or which ones are overburdened, and allows organizations to optimally allocate the resources. This helps organizations to minimize waste and improve productivity.

Conclusion

Process intelligence allows organizations to enhance their efficiency, reduce costs, and improve customer experiences. It enables businesses to stay competitive and relevant in today's continuously changing business environment. Let's move ahead to the next chapter and learn about the benefits of process intelligence.

Benefits of Process Intelligence

Process intelligence is one of the significant approaches used by business organizations to optimize their operations and remain competitive and relevant in continuously changing business environment.

Process intelligence offers several advantages by combining multiple advanced technologies such as process mining, process discovery, advanced data analytics, and continuous monitoring of processes.

This chapter provides a comprehensive overview of some of the key benefits or advantages of process intelligence when it is integrated in business processes and workflows.

Benefits of Process Intelligence

The key benefits of process intelligence in business organizations are described here −

Data-Driven Decision Making

Process intelligence allows business organizations to collect and analyze their process data in real-time, and this data is then utilized to provide actionable insights to make informed decisions.

Data-driven decision making helps organizations to identify inefficiencies and bottlenecks in their workflows and provides capabilities to perform predictive analytics of process data to forecast risks and issues in advance.

Process Intelligence provides reliable decision-making capabilities based on actual process data instead of assumptions and enables to reduce uncertainties in business operations.

Automation

Process intelligence enables organizations to determine areas of automation. Hence, it helps business organizations to identify repetitive, low-value, and time-consuming operations or workflows for automation.

Additionally, process intelligence provides process performance analytics that business organizations use to optimize their existing process automations by eliminating inefficiencies.

Enhanced Compliance and Risk Management

Compliance with regulatory standards is one of the important concerns for many businesses like financial institutions, healthcare industries, and manufacturing units. Process intelligence helps these industries to continuously monitor their processes to ensure that they adhere to regulatory standards. Therefore, it reduces the risks of compliance violations, reputation damage, and penalties on business organizations.

Increased Efficiency

Process intelligence plays a significant role in making business workflows efficient. It improves the operational efficiency of business organizations by streamlining operations, reducing cycle times, eliminating unnecessary steps, and optimizing resource utilizations.

Continuous Improvement

Process intelligence provides several advanced capabilities like real-time monitoring, advanced performance analysis, etc. These practices encourage continuous improvement and refinement of processes and workflows, and helps organizations to stay competitive and relevant.

Cost Savings

It is one of the most significant advantages that process intelligence offers to business organizations. The optimization of business processes and workflows naturally reduces costs involved in their execution. Also, process intelligence enables organizations to reduce waste and minimize error-related costs.

Increased Customer Satisfaction

For any business organization, customer satisfaction is the top most factor to succeed. Process intelligence helps organizations to enhance their customer satisfaction by providing consistent quality service, increasing response time, providing personalized customer experiences, and reducing errors in service delivery. All these practices build the trust of customers on businesses and boost their growth.

Enhanced Visibility

Process intelligence brings the transparency in business operations. It provides detailed information about how processes actually execute and how they interact with their dependencies. This helps proactively rectifying issues that may otherwise disrupt the operations.

Enhanced Workplace Productivity

Process intelligence also improves the productivity at the workplace. It provides insights to automate low-value, repetitive tasks and free-up employees to be engaged in high-value, strategic operations. It also provides enhanced team collaboration by streamlining processes. Process intelligence also makes decision-making better and faster.

Conclusion

In this chapter, we explained some of the major advantages that process intelligence offers to a business organization. In conclusion, process intelligence is one of the important tools for transforming business operations, from decision-making to reducing risks and improving customer experiences.

Process Intelligence Challenges

It can be a challenging effort to implement Process Intelligence in a business organization. These challenges can be related to data privacy concerns to employee's resistance to changes and availability of resources. Each of these challenges makes it difficult to adopt and implement process intelligence.

This chapter highlights the major challenges in process intelligence implementation and will try to provide causes and strategies to reduce them.

Challenges in Process Intelligence Implementation

The key challenges that business organizations face while implementing process intelligence are explained below −

Data Privacy Concerns

It is one of the most significant challenges in process intelligence. As we know, data collection and analysis are the foundations of process intelligence. Business organizations have several sensitive data like financial records, personal information of employees, intellectual properties, etc. that must be confidential and protected securely. However, process intelligence, being distributed across various departments, can lead risks of data breaches or misuse.

The primary cause behind unauthorized data access is the improper implementation of access control to sensitive data within the organization. However, by implement proper data encryption, masking personal data, and ensuring adherence to compliance can significantly reduce the data privacy concerns in process intelligence.

Resistance to Change from Employees

The adoption or implementation of new technologies like process intelligence within a business organization is resisted by employees due to fear of job displacement, learning complexities in new technologies, changing job roles, etc. However, this challenge can be addressed effectively by educating employees on benefits of process intelligence and encouraging them to use it as a collaborative tool to enhance their work capabilities.

Lack of Clear Vision

Sometimes, business organizations find themselves incapable in understanding how process intelligence will align with their business objectives. This impacts the successful implementation of process intelligence within the organization.

The main cause behind this challenge is starting implementation of process intelligence without having a clear objective and roadmap. Although, businesses can overcome this issue by defining a clear goal for process intelligence according to their business needs and making necessary changes based on feedbacks.

Resource Constraints

Process intelligence requires significant resources like technologies, tools, investment, and experts for successful implantation. Making these resources available becomes a challenge for small- and mid-sized business organizations.

However, by adopting some solutions like using cloud-based tools and technologies, taking freelance expert services, investing in high-impact processes first and then scale gradually, etc. can play an important role in addressing this challenge and implementing process intelligence.

Inadequate Data

Process Intelligence is a data-driven technology. The operation of process intelligence systems is completely dependent on accurate and detailed process data. If the available data is inadequate or of low-quality, then it can result in poor-quality performance analytics, inaccurate insights, and ineffective recommendations on process optimization.

This challenge can be effectively mitigated by following practices like data cleaning, integrating data from various sources, and implementing data governance policies.

Conclusion

In this chapter, we provided a basic overview of the kind of challenges in process intelligence implementation such as data privacy concerns, data quality issues, resistance to change, etc.

Business organizations can try to address these challenges with well-planed strategies to implement successful process intelligence and drive business excellence and long-term sustainability.

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