Process mining for sentience in business processes

A huge amount of data is getting created daily across the world. As we move towards a data-first world, businesses need to be able to process the data in real-time to provide reliable and relevant data consistently to all stakeholders. With the huge surge in the demand for digital services, which was accelerated by the pandemic, businesses need to expedite digital transformation, and harness the power of AI and analytics to transform themselves into data-driven enterprises.

Automatic data collection has led to an ever-expanding digital environment, and while data analytics provides many answers, it often does not provide relevant answers to pertinent questions from business users. The vast amount of data results in better collection and control, but data is still often in silos, and does not necessarily create an intelligent business enterprise. Agility, responsiveness, and proactivity are key to business operations, and analytics alone cannot do that.

The path to an intelligent and sentient enterprise

Smart devices enabled with IoT, and the sheer magnitude of created data has enabled digital twins, which represent an accurate digital clone of a real-world entity or system. Digital twins have widespread usage across industries; from digital models of aircraft engines during a flight for monitoring engine health, to virtual representations of an entire city, the usage is wide and varied.

To create sentient enterprises*, companies need to connect process management with business intelligence and analytics, and be able to understand how a process is performing on a day-to-day basis.  Businesses can create digital replicas of real-world processes, which are process digital twins. These process digital twins can simulate real-world business operations. When used with process mining technology, this can lead to process excellence, and eventually, truly intelligent enterprises.

With process mining, businesses can create the much-required connect between operations and business process management, and data science (AI, ML and predictive analytics). Process mining software captures information at a transactional level. Event logs are created for each transaction, and key parameters are captured. For example, it may capture the time taken to place an order, make a payment, and deliver a product. Typically, the logs would also capture details of events happening outside the digital system, where manual intervention is required, thus enabling analysts to aid in process improvement. The business needs to identify the key performance indicators that require to be tracked during process mining, so that the most relevant insights can be obtained.

Process mining offers several benefits. Significant issues can be identified during process mining. For example, process mining an order-to-cash process (O2C) could reveal that important customers were placed on a credit hold for an extended period of time. Cross-functional teams can get an end-to-end view of the entire process, offering better clarity in terms of roles and responsibilities, and improvements required for collaboration. With process mining, workflows can become more transparent. It can reveal exceptions and deviations from the norm, for both simple and complex processes, and thus the opportunity to improve the process.

The difference between process mining, data mining and BPM

Although it seems like process mining, data mining and business process management (BPM) are nearly the same, there are distinct differences. Process mining is a bridge between data mining and BPM. Process mining uses event logs for a specific process. These event logs are used to generate process models that can be used for discovery, comparison, and enhancement of processes. Data mining uses various datasets for predictive analytics such as market analysis, weather forecasting etc. On the other hand, process mining lends a data-driven approach to BPM, which is traditionally a qualitative rather than a quantitative method.

The importance of process mining

With process mining, organisations can quantify specific process inefficiencies. With an evidence-based methodology, organisations can implement new process models or tweak existing processes, to arrive at process improvements. These may take many months, but the process enhancements result in reduced cost, improved quality, and higher customer retention. 

Process mining will help the organisation become more agile, and lead to improved and strategic decision making. It will also result in better predictions and risk evaluation and ensure rapid responses to change. Agility is crucial for sentient and intelligent enterprises. Data-driven decision making with process mining and leveraging new technologies such as AI and machine learning will drive organisations towards innovation and business excellence.

*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.

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