As per Deloitte research, In 2021, 73% of organizations will have begun their intelligent automation journey, a 58% increase from 2019 figures. As more and more businesses strive to use automation at scale, many are realizing they must also streamline and optimize business processes to fully realize automation’s massive advantages.

This has subsequently led to the development of technologies such as task Mining, and process mining. So, what are process mining and task mining, and how are they different? Both aid in mapping an organization’s task and process hierarchies, so that you know how and why to automate.

But process and task mining are different from each other and perform distinct (if complementary) functions.

What is Task Mining? Definition and Working

Task mining utilizes desktop-level knowledge and unstructured data, to help enterprises create assets on how workers or users carry out the multiple actions that comprise a business process.

How does task mining work?

It operates at the desktop level to identify and analyze the tasks performed by users in between enterprise-level processes. This is accomplished by the installation of a localized agent on every desktop that captures users ’ interactions (keystrokes, mouse movements, etc.) and combines this information with context recognition to comprehend how tasks are completed and the variances that occur among groups.

Significantly, data privacy and associated risks are always considered prior to do this.

To successfully improve processes, it is not sufficient to just have insight into them; you must also understand how and where they occur. The purpose of task mining is to accomplish this. It connects to user workstations and related interfaces in order to collect user activity data, frequently in an unstructured manner.

The system then employs optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) to sort through the information and deliver meaningful inferences.

Now that we’ve covered the meaning of task mining, let us consider process mining and its role in automation.

What is Process Mining? Definition and Working

Process mining is the analysis of organizational data (often organized and transactional in form) to identify the hierarchy and linkages of existing business processes.

As with task mining, process mining offers visibility and data-driven information. Without it, businesses would have to depend on intuition and subjective understanding, and there is the danger of automating incorrect procedures. In fact, this is frequently the case.

How does process mining work?

Using transactional and log data from multiple business systems like CRMs, ERPs, human resource management systems, supply chain management systems, etc., process mining generates a visual map of the organization’s process architecture.

It leverages data from event logs (process mining input) to generate process maps (process mining output). A data source may be any log holding information about a completed action (such as purchase order creation), its case (purchase order No. 12345), and the time it occurred. The process map represents the actual process flow, including all instances, variations, and pathways, as they happen.

It should be highlighted, however, that this technology is limited to transactional systems and can’t offer insight into a) user actions and b) the fundamental activities that constitute a process.

In this regard, it differs from task mining.

5 Key Differences Between Process Mining vs. Task Mining

Process mining differs from task mining in the following key aspects:

1. The data source used

Process mining harvests corporate data saved as incident log data in IT systems and online applications, including CRM, ERP, and HubSpot. Consequently, it provides insight into the implementation of a procedure by displaying the steps taken, length of activities, and occurrences of deviations.

Task mining gathers desktop data by observing and recording user behaviors. By collecting and displaying workstation data, organizational management can more effectively evaluate employee performance and find inefficiencies that can be removed with automation.

2. The type of inefficiency revealed

Process mining offers high-level insights into potential inefficiencies. For instance, they may disclose that the identical operation is occurring in two distinct business units, resulting in redundancies. You may analyze these inefficiencies further by conducting a thorough examination of desktop-level data. Task mining will reveal which applications are being utilized, which procedures are repeated, and a lot more – i.e., inefficiencies at the individual level.

3. Granular content of the output deliverable

A process consists of events and subtasks with distinct beginning and ending points, like the processing of invoices. Process mining examines these occurrences and depicts the entire process, comprising subtopics, activities, and timeframe.

In contrast, tasks may either include a single function or link many processes. Copying and pasting data from one source to another or uploading files are examples of manual chores. Task mining exposes these individual tasks by revealing information on the action and steps taken during their execution.

4. The use of unique identifiers

A crucial characteristic of process mining is that it assigns a unique identification number or code to each process instance, often known as a case ID. There is no specific identifier for each stage in task mining. Companies often combine process mining with task mining due to this distinction. This makes it easy to comprehend exactly where an action or job fits within the process architecture of the organization.

5. The technology used for data collection

Using machine learning (ML) algorithms, process mining interacts with IT networks to gather data. After data has been obtained, cleansed, and processed for evaluation, the process mining tool depicts the entire process. With the use of ML algorithms as well as data science techniques, some tools may even compare it to the ideal model. On the basis of the results, analysts are able to identify blockages or deviations and make adjustments and re-designs.

In contrast, task mining is based on a user’s desktop and, once authorized, captures everything in the background. Computer vision, rules mining, text analytics, natural language processing, and OCR may all benefit task mining.

By documenting the activities and actions of users, analysts are better able to determine the root causes of inefficiencies and deviations.

Process Mining vs. Task Mining: Examples

External and internal auditors are terrific examples of process mining. It may offer rapid and thorough insights into the organization’s current and historical procedures. This allows auditors to move from biased or heavily subjective sample analysis to accurate, comprehensive as-is examination – to prepare the automation-readiness assessment report.

The best use of task mining is seeing how workers complete tasks on their computers. It also employs OCR when capturing user behaviors in order to recognize their contexts.

It does this by collecting words, figures, as well as other data from recordings and screenshots made when a user is interacting with an application and creating a map of these against the user’s clicks, scrolling, and other inputs. This ensures that the automation is actually in line with what the employee needs.

Process Mining vs. Task Mining: Which One Do You Need?

Process mining and task mining can be used together or separately. Separately, each has its unique value in the:

• Task mining is more suitable for executing automation: When it comes to spotting opportunities and advancing the adoption of automation, task mining has the advantage. If the objective is to automate processes that are now performed manually, it is essential to have granular information about manual labor. Here, task mining really excels.
• Process mining is more suitable for system migrations: Because of the manual mapping necessary to capture as-is processes, major migrations (e.g., moving to SAP S/4HANA) have historically consumed countless hours and exorbitant costs. Process mining reduces the amount of human labor required for these migrations by analyzing system data dynamically.

In actuality, there is no contest between processing mining and task mining; the two techniques are complementary. When used in tandem, you can truly understand workflows — in their granular entirety —  and then optimize what you currently have by analyzing handoffs, resource utilization, and other factors – by building automation systems.

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