Process Mining as a Precursor to Digital Transformation

7Mar - by aiuniverse - 0 - In Data Mining


Creating a digital transformation roadmap is not a wise idea without assessing the current state of business processes. A typical business process mapping for any enterprise would start with analyzing the current business process, shadowing users during the task, and questioning them about the same. Some enterprises may also extend to a visual walkthrough of the user screens for more in-depth process analysis.

But, despite analyzing the business process to the core using this typical business process analysis approach, all that these enterprises have achieved are low measurable results leading to failed automation efforts and unsuccessful transformation!

The question is – “Why?”

The one fatal flaw that led to the failure of digital transformation among these enterprises is incomplete or improper enterprise business process analysis. How could you think of transforming the business process if you were not aware of the process details and variants?

The typical process mapping and even the more updated current process mining miss out on the nuances and the several layers embedded deep within the enterprise business processes.

What Hinders Complete Digital Transformation?

RPAs, big data, cloud computing, Machine Learning, and Artificial Intelligence; despite having all these intelligent tools at their end, many enterprises are still unable to achieve the much-needed digital transformation.

One of the biggest reasons behind this is the inability to understand the nuances of business processes.

The old-school way of analyzing all aspects of the business process revolves around human interview-based discovery. However, it is difficult to translate every nuance into a traditional process map. Furthermore, if a processing activity involves multiple participants across locations using numerous locations, the scale and the precision necessary to document a process goes up exponentially.

To successfully leverage the emerging digital technologies and gain an edge with digitally optimized processes, you must discard the traditional way of documenting and analyzing business processes. And more importantly, embrace the digitally powered approach to business processes mapping. We are, of course, talking about process mining and process discovery.

Analyzing Processes using Artificial Intelligence

Enterprises, being large and complex data hubs, cannot achieve digital transformation solely on the human-interview based business process discovery. In fact, digital transformation via disruption is also not considered a good idea. So, what should they do to undergo a true digital transformation?

They need to unearth the hidden and invisible facets of business processes for a real transformation. Here are some ways enterprises can capture and analyze their business processes:

● Computer Vision & Image Processing

Ever tried searching a query on Google by dropping an image in the query box? Or uploaded a pic on Shutterstock to find a similar picture from the 70 million photos there? If not these, you must have tried unlocking your smartphone through facial unlock!If yes, you are already a user of computer vision.

So, how is it done?

This is done through computer vision: a branch of Artificial Intelligence that enables the computers to analyze and interpret images through deep learning. Computer vision algorithms and models are trained to know what various objects look like and differentiate one from another.

In computer vision and machine intelligence-based process mining, it starts with recording every human interaction with a system into an image. Not only are the images scanned and visualized, but they are also processed through layers of the neural network to compute what the image truly contains.

Using this technology to digitally observe and interpret the interactions between humans and machines at work, analyzing business processes could become more deep-rooted. Well-trained neural networks are put to use for recognizing the screens, applications, and tasks to identify changes in operations. Depicting the work actions accurately through this process enables a more efficient automation strategy.

● Capturing Human-Machine Interaction

Polanyi’s Paradox tells us that “We can know more than we can tell.”

True indeed! No matter how deep the conversation, humans tend to undertake over 90% of their decisions, interactions, and experiences in a non-verbal form.

If you do not have a clear understanding of the current state of business processes, creating a roadmap for digital transformation would be impossible. Without focusing on the Invisible Enterprise, you might just be there with a failed digital transformation.

You can overcome this by capturing all aspects of the human-machine interaction at work. It would eradicate the superficiality or half-received knowledge of the business processes. When you have a clearer idea of how your employees are interacting with the machines at your workplace, transforming the process of interaction through digital means and tools would become more straightforward. Your foundation for digital transformation would be factual and lead to more fruitful endeavors.

Deep Neural Networks

Machine Learning gave birth to Deep Learning, a way of teaching machines to become smarter in their way of working. To learn through the different layers of data, you must form links or patterns in the unstructured data.

A deep neural network is that form of machine learning that detects these patterns. As a brain neuron receives a signal, interprets it, and creates a response for another neuron using the same, deep neural network works in the same manner. They act as brain neurons in deep learning.

Not only do deep neural networks analyze the data generated around your business processes but also remove any noise around the data to unveil what the real intent behind the particular work action is.

When analyzing business processes, deep neural networks can skim through the various layers of unstructured data, understand its relevance and connections, and predict its future implications. Assuming that enterprises have large and complex data sets, deep neural networks can be an efficient way to quickly recognize, categorize, and predict future outcomes for digital transformation.

Decision Mining

What’s better than mining millions of bits of data for business process analysis? Analyzing what leads to the choices made in the business process!

In simple words, business process analysis could be made extensive yet simplified by analyzing how people in the enterprise make decisions. And decision-mining is all about that!

The key to decision mining is knowing the decision points. You need to know what alternatives the employees had and the possible choices. Every choice in the process model is analyzed to see the available options during that point in the event log. This helps in explaining what influenced that choice.

Having complete knowledge of the decision points, influencing factors, and available alternatives could help in improving the way real-life decisions are made during the process. The real understanding of the business decisions could come in handy for process re-designing and re-engineering within the context of digital transformation.

Summing It Up

An AI platform that combines machine learning, data sciences, computer vision, and graph theory to paint a holistic picture of your business process is the foundation for successful digital transformations and optimal automation.

Process Discovery could help you map your business processes accurately, capture your every interaction, and uncover all the nuances in the process while continuously learning and improving itself. It can help enterprises analyze their operations to the core and orchestrate a roadmap for real digital transformation.

Key Takeaways

  • For enterprises to attain digital transformation, it is imperative that they analyze their processes to the core. Process mining and process discovery are the best ways to know the true state of their processes.
  • Computer vision is a part of process discovery that enables computers to analyze and interpret images through deep learning.
  • Capturing each interaction between the employees and the machines can be helpful in knowing the hidden processes.
  • Unstructured data from your enterprise’s processes transformed into structured data for analyzing and detecting patterns or anomalies using deep neural nets
  • In addition to mining data, enterprises also need to mine the decision process to reach a better strategy in context with your digital transformation efforts.
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