Process intelligence is where the art of intelligent processing and the science of decision-making meet. In the realm of business management, it is the overlap between business intelligence and business process management.
Process intelligence leverages tools to simplify business processes and data to gain valuable insights that drive better business outcomes.
What is process intelligence?
Process intelligence is the practice of collecting and analyzing data related to business processes and operational workflows. By systematically analyzing data, organizations can identify areas that are performing well and those that need improvement.
Process intelligence provides a comprehensive view of the entire business operation across multiple departments, consolidating the information into actionable insights and reports.
The streamlined operations enabled by process intelligence can lead to more efficient use of resources and improved bottom lines, allowing organizations to remain competitive in responding to current market challenges.
This practice often involves using digital and artificial intelligence (AI) tools, such as data mining and machine learning algorithms, to analyze event logs and identify process models, variations, bottlenecks, and inefficiencies. Other AI techniques, such as natural language processing and computer vision, can also be used to analyze unstructured data like text and images.
Why is process intelligence important?
Businesses run on a myriad of processes, sub-processes, and tasks that span recruitment, onboarding, training, operations, delivery, and financial operations.
Processes are how products are made, packaged, priced, sold, shipped, delivered, invoiced, and inventoried. It is how services are offered, performed, billed, and paid for. Processes govern reporting, managing, maintaining, and auditing. In short, processes keep the business alive.
There have been various approaches to gaining business intelligence and business process management. Consider, for example, a candy store that’s popular with the kids of a neighborhood. The management of the operational processes of the candy store depends on its scale of operation.
- Process mapping is the approach to business intelligence and process management when the candy store is a small mom-and-pop shack in the garage of the owner’s house. This is because the processes are simple, and there aren’t many steps involved. Process mapping is a manual method used to understand processes. In the case of our candy store, process mapping involves counter talk with the kids (or their guardians who hold the purse strings) and feedback from the maker of the candy, the salesperson at the counter, and the cashier. However, as the candy store grows, process mapping can become slow, expensive, and unsuitable.
- Process mining uses technology to record the workflow through every step based on user interactions with specific systems. It is a data analytics technique that allows organizations to analyze their business processes using event logs recorded by their information systems. For the candy store that has expanded its operations to include an online store or a computerized point-of-sale system, process mining can help to identify and track processes and inefficiencies.
- Process discovery is a more advanced form of process mining in which advanced software tracks workflow through the main and subprocesses. It involves identifying patterns in the data and creating process models to visualize and analyze the actual process flows within an organization. Process discovery can be particularly useful for the candy store that has now grown to have many departments or has expanded its product line.
While conventional methods like process mapping, process mining, and process discovery provide some understanding of business processes, they fall short of delivering comprehensive insights.
To make informed decisions, it’s crucial to access 100% of process data, including every discrete step, but the vast amount and complexity of data make it challenging to handle all the available data without assistance. Process intelligence involves the use of advanced digital tools and AI-powered methods to capture granular details from vast amounts of data, ensuring scalability, accuracy, speed, unbiased results, and continuous process capture, enabling organizations to get instant visibility into the context and meaning behind their process data. Process intelligence can provide metadata for meaningful insights, unbound by geography, applications, teams, or specialties.
To complete our example of the candy store that is now a roaring success, process intelligence can help the now international candy store by providing insights into the business processes involved in managing the store, from supply chain and inventory management to sales and customer service. With process intelligence tools, the store can identify inefficiencies, streamline processes, and make data-driven decisions to optimize its operations.
For example, it can analyze data on customer behavior to identify which products are popular where and at which part of the year (obviously February), which are not selling, and adjust inventory levels accordingly. It can also track order processing times, identify delays and constraints in the supply chain, and adjust delivery schedules to ensure the timely and efficient delivery of products. Overall, process intelligence can help the candy store improve its operations and profitability.
Advantages of process intelligence
Process intelligence provides a clear visual model of the workflow throughout the entire process, enabling businesses to identify incompetence and/or inefficiencies. With real-time visibility, businesses can rectify and eliminate these issues, thereby leaving more room for best practices and improvements. Process intelligence software can monitor and analyze processes on a historic or real-time basis, making it especially useful for analyzing and managing nonlinear processes with many dependencies.
Process intelligence goes beyond traditional process mapping, mining, and discovery methods in the following crucial ways.
- It is scalable, allowing businesses to capture granular details of all processes across the organization to find improvements with the most impact.
- It is accurate, combining cross-application data collection and AI modeling to provide a high level of data-driven detail that enables confident strategic decision-making.
- It is fast and eliminates slow and tedious manual steps, improving time-to-value, and return on investment.
- It is unbiased and continuous, eliminating bias or errors of manual capture and enabling workers to continue without influence or distractions. This added accuracy prevents rework that slows down digital transformations.
Tool used in process intelligence
Process intelligence involves using a variety of tools and techniques to capture and analyze data from various business processes. The tools used in process intelligence can vary depending on the organization’s specific needs and goals, but they all focus on using data to optimize business processes and drive continuous improvement.
Here are some common tools used in process intelligence:
- Process mining software: This software extracts data from different IT systems to discover, monitor, and improve business processes. It provides insights into process variations, bottlenecks, and inefficiencies, and can identify the root cause of problems.
- Robotic Process Automation (RPA): RPA tools automate repetitive, rule-based processes, freeing up human workers to focus on more complex tasks. They can be used to create digital twins of human workers that can perform tasks such as data entry, invoice processing, and customer support.
- Business Process Management (BPM) software: BPM software is used to model, automate, and optimize business processes. It provides a visual representation of a process, including its various steps, actors, and data inputs and outputs.
- Artificial Intelligence (AI) and Machine Learning (ML): These technologies analyze process data, predict outcomes, and identify patterns and anomalies. AI and ML can help organizations automate decision-making and optimize processes in real-time.
- Data visualization tools: These tools help to make sense of complex process data by presenting it in a visual format, such as charts, graphs, and heatmaps. Visualization tools can help identify trends, outliers, and areas for improvement.
- Process simulation software: This software simulates different process scenarios to identify the most efficient and effective process design. It can help organizations identify and test process improvements before implementing them in real life.
What are use cases for process intelligence?
Any sector that is data intensive is best suited to adopt process intelligence. These days data is the clay to make the bricks of any business, and therefore, there is no sector that would not benefit from process intelligence. Some of the industries where process intelligence can be of great help include
Process intelligence may be considered the next avatar of the assembly line style of work in improving efficiency, reducing waste, and improving quality in the manufacturing process. With process intelligence, manufacturers can collect data from across their production lines and analyze it in real time, allowing them to identify patterns and trends that may not have been apparent before.
By providing a real-time view of processes, manufacturers can ensure that they are meeting all requirements and taking appropriate corrective actions if needed. This helps to prevent errors and minimize risk, which can save a lot of time and money in the long run. Process intelligence can also be used to improve supply chain management by providing visibility into the movement of goods and materials. By tracking inventory levels and shipment status, manufacturers can optimize their supply chain to reduce costs and ensure that they have the resources they need when they need them.
Process intelligence can be used to optimize the patient registration process, reducing wait times and improving patient satisfaction. By analyzing data on patient visits and appointments, healthcare providers can identify patterns and trends, helping them to better allocate resources and manage patient flow. Process intelligence can also be used to improve clinical decision-making. By capturing data on patient outcomes and treatment protocols, healthcare providers can gain insights into which treatments are most effective and make adjustments as needed.
Banking and finance
Process Intelligence can help the banking and financial sector gain a comprehensive understanding of business operations, detect fraud, and improve customer experience. With the vast amount of data generated in the banking sector, process intelligence helps to process, aggregate, and analyze the data to detect anomalies, flag suspicious activities, and prevent fraudulent transactions.
It also helps in enhancing customer experience by providing real-time insights, predicting customer behavior, and identifying areas that need improvement. With process intelligence, banks can automate and optimize their processes, leading to cost savings, increased productivity, and improved customer satisfaction.
Logistics and transportation
By using data and analysis tools, process intelligence can help to optimize the supply chain, reduce costs, and increase efficiency. It can help to track shipments and identify the most efficient routes, as well as to monitor the performance of suppliers and identify areas for improvement.
Process intelligence can also provide real-time visibility into inventory levels and help to optimize warehouse management.
With the increasing demand for transparency and accountability, public organizations and government bodies can use process intelligence to analyze their processes, identify inefficiencies and make data-driven decisions to optimize their workflows.
Process intelligence can help to streamline bureaucratic processes and provide insights into how to allocate resources efficiently. It can also help in identifying potential areas for fraud, waste, and abuse, thereby reducing the risk of corruption. Furthermore, process intelligence can assist in improving citizen engagement and satisfaction by offering faster and more efficient services.
Implementing process intelligence in your business
Implementing process intelligence in an enterprise can drive efficiency and improve bottom lines. However, it is essential for each business to first have a clear understanding of what they hope to achieve with process intelligence.
Whether it’s identifying bottlenecks, streamlining processes, or improving customer experience, having specific goals in mind will help guide the implementation process and ensure that the desired outcomes are achieved.
Once a business has a clear understanding of its objectives, it can then begin to explore the various tools and methods available for implementing process intelligence, from process mapping and discovery to advanced analytics and machine learning.
Implementing process intelligence in an enterprise involves several steps:
- Defining the scope: Identify the processes to be analyzed and determine the specific goals of the analysis.
- Gathering data: Collect data from various sources, including legacy systems, databases, and other relevant software applications.
- Cleaning and consolidating the data: The data gathered from various sources should be cleaned, consolidated, and standardized to ensure its accuracy and consistency.
- Analyzing the data: Use process intelligence tools to analyze the data and identify patterns, trends, and areas for improvement.
- Developing action plans: Based on the analysis, develop action plans to optimize the identified processes, reduce inefficiencies and improve performance.
- Implementing changes: Implement the proposed changes and monitor the results to ensure they are effective.
- Continuous monitoring and refining: Continuously monitor the processes and refine them to improve efficiency and effectiveness.
It is important to involve key stakeholders, including employees, in the process to ensure their buy-in and support for the changes. Additionally, organizations may choose to work with a process intelligence vendor or consultant to assist with the implementation process.
Process intelligence powered by Nanonets
As mentioned numerous times in this article, process intelligence runs on data.
Data extraction is a crucial step in the process intelligence workflow as it involves gathering data from various sources, such as databases, documents, and logs, to create a comprehensive view of the business process. The extracted data is then analyzed using process intelligence tools to generate insights into how the process operates, and how the process can be optimized for better performance. Without accurate and comprehensive data extraction, process intelligence analysis will not be able to provide the required insights to optimize business processes.
This is where Nanonets comes in.
In the context of process intelligence, the Nanonets’ technology can be used to automate and streamline the data collection and analysis process, enabling organizations to gain insights and make informed decisions in a timely manner.
By leveraging AI and ML, Nanonets can help organizations extract valuable information from unstructured data sources, such as emails, invoices, and customer feedback, which can be challenging to process manually.
Process intelligence is a game-changer for businesses across various industries. With the ability to extract insights from vast amounts of process data, organizations can make data-driven decisions, improve operational efficiency, and enhance customer experiences.
From healthcare and finance to logistics and transportation, the potential applications of process intelligence are virtually endless. With the increasing demand for automation and digital transformation, it is becoming essential for enterprises to adopt process intelligence tools and techniques to stay competitive in today’s rapidly evolving business landscape. Those who embrace this technology and use it to their advantage will have a significant edge over those who do not.