What Is Process Intelligence – And Why Is It Important?

Process Intelligence

What Is Process Intelligence – And Why Is It Important?

Every business enterprise runs on business processes. Effectively, processes exist in every industry domain and can be extended to every part of any business. This includes functions like workforce hiring and training, company finances & accounting, customer services, and much more. Efficient business processes enable companies to achieve their desired goals.

On the other hand, inefficient processes can create bottlenecks and increase operational inefficiencies. Therefore, process intelligence is important. What is process intelligence – and what business value does it offer? Let’s discuss.

What is Process Intelligence?

As business enterprises emerge to depend heavily on data, any transformation initiative will not be successful without high-quality data. McKinsey reports that only 14% of business transformations have improved performance, while 70% of enterprise programs do not achieve their goals.

Process intelligence is simply the ability of companies to know and understand their business processes. It is not possible without measuring and managing processes. As management consultant Peter Drucker says, “If you cannot measure it, you cannot improve it.”

Process intelligence involves the practice of collecting and analyzing process-related data to identify bottlenecks and improve efficiency. Typically, process intelligence allows business analysts to:

    • Explore the workflow steps
    • Identify who oversees the process
    • Measure the overall duration of the process and average wait times
    • Identify problems with the process

Typically, process intelligence comprises the following:

    • Process mining
    • Process discovery

What is process mining – and how is it different from process discovery? Traditionally, process mining is the backend technology that records the stepwise workflow based on user inputs and interactions. Process discovery is the latest alternative to process mining, which tracks the workflow at every interface level, irrespective of who performs the task or which application is being used.

Next, let us see why process intelligence is important for any organization along with its value proposition.

Read, Also – Why Is RPA Important for Modern Business? 

Why is Process Intelligence important?

Process intelligence enables organizations to acquire process-related data continuously and automatically across all enterprise systems. This technology is useful in providing clear visibility into your business processes, which can later help in improving business process automation (BPA) and digital transformation.

Effectively, process intelligence enables organizations to understand their current processes and plan for future processes. How does process intelligence enable organizations to gain operational visibility? Here is how to process intelligence works:

  • Leverages technologies like Artificial Intelligence (AI) and Computer Vision to create a template of all organizational processes across all business functions and departments
  • Creates a detailed process definition document (PDD) automatically
  • Captures the process data with all its details

Thus, process intelligence removes the “guesswork” from understanding business processes and replaces it with a data-driven alternative. Besides data-driven decision-making, process intelligence can benefit organizations in the following ways:

1. Identify and eliminate bottlenecks and inefficiencies

Using process intelligence tools, organizations can analyze and visualize their business processes. For instance, you can pinpoint the exact bottleneck in any process workflow along with deviations from the standard practice. By analyzing the root causes of the problem, organizations can improve the process and eliminate any inefficiency.

2. Optimize your costs

Process intelligence helps in eliminating process bottlenecks and deviations, which leads to a reduction in operational costs. This helps organizations in optimizing their costs and assigning an additional budget to other parts of their business.

3. Enable process improvement

Traditionally, process improvement initiatives are regarded as long and time-consuming as a result of repetitive data capturing, cleaning, and mapping tasks. Process intelligence can reduce this time by automating process improvement. The use of automation reduces human intervention and manual errors, thus saving time and costs.

Process intelligence is also about process discovery, also referred to as knowledge discovery. Next, let us discuss the knowledge discovery process in data mining.

The business value of Knowledge discovery

With the growing volume of business data, data mining is now an essential system that can “make sense” of the available information and generate reports automatically. The data mining process (or knowledge discovery) enables businesses to make better-informed decisions by:

  • Summarizing data automatically
  • Extracting the true essence of the stored information
  • Discovering useful patterns in the stored data

What are the four major steps in the data mining process?

Here are the steps involved in the Knowledge Discovery in Database or KDD process in data mining:

  1. Cleaning data – or removing any irrelevant data from the collection
  2. Integrating data – or the step where heterogeneous data is integrated from many sources into a common data warehouse
  3. Selecting data – or the third step where data that is relevant to the analysis process is retrieved from the collection
  4. Transforming data – or the final step where data is transformed to the right form, where it can be used for mining.

Process Intelligence – Our perspective

At NuMantra, we deploy hyperautomation (driven by AI and machine learning technologies) to analyze data and prepare it for effective predictive analytics. By combining machine learning/AI based models with robotic process automation (RPA), we simplify complex workflows using predictive insights to support desired business outcomes.

Also referred to as predictive modeling, predictive analytics uses AI and machine learning techniques to predict business outcomes in the future based on the current data. AI-based predictive analytics has a host of industrial applications including:

  • Supply chain management
  • Product quality control
  • Customer buying behavior
  • Risk management
  • Fraud detection

To summarize, process intelligence is a combination of process (or data) mining and knowledge discovery. Process intelligence offers much more business value than “traditional” process mining with benefits like scalability, integration, accuracy, automation, and continuous improvement.

At NuMantra Technologies, we strive to maximize the benefits of process intelligence – with a deeper dive into process mining and knowledge discovery. Our process mining application uses pre-built algorithms to identify business trends and patterns and obtain clarity over the health of your business processes. Effectively, we use process mining and machine learning techniques to gather and analyze internal and external data.

With our Robotic Process Automation or RPA platform, we ensure that our solutions driven by in process intelligence enable our customers to pursue a sustainable path to realize their business potential. Our RPA expertise enables our customers to accelerate their organizational processes by automating all manual and repetitive actions.

How can we help in optimizing your business processes? Request a product demo today.

Read, Also – Why hyperautomation is advancing finance and how it can benefit your organization

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