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Task Mining in '24: What it is & How it works in 5 Steps

Updated on Mar 10
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Task mining allows businesses to understand the way they handle tasks by monitoring user interactions. Although it is not a popular term yet and attracts only a moderate amount of interest according to Google Trends, we expect it to gain popularity, given that it provides a more in-depth look at processes than other technologies.

The detailed understanding provided by task mining can help companies improve and automate processes. Task mining shares similarities with process mining, but it leverages user interaction data instead of business metrics and log files to analyze processes. Today, leading process mining vendors also offer task mining solutions, including them in their free trials.

Since task mining records user interactions on the computer in real-time, it can also violate user privacy. Thus, these tools also need to focus on data privacy and protect users’ personal data. As this technology will become more popular, this issue will be one of the main challenges ahead for task mining vendors.

What is task mining?

Task mining is an emerging technology that enables companies to understand how they perform their tasks by monitoring user actions and collecting user interaction data. From the insights gained, businesses can observe how they handle processes, identify the most common mistakes while performing tasks, and discover tasks that can be automated. As a result, it is a way to improve business processes and enhancing automation.

Today, most task mining solutions are integrated with process mining technologies to understand processes better and find ways to enhance the whole process rather than just improve how employees perform specific tasks. For instance, Celonis process mining studio includes task mining capability. Figure 1 shows the task mining dashboard where analysts can view the data captured by the tool and several KPIs, such as user productivity or copy & paste behavior, required to assess employee performance.

Figure 1: Task Mining Analysis

How does task mining work?

Here is how task mining works step by step:

1. Record user activities

Task mining tools record user activities to understand how employees handle their tasks. These tools are installed on every employee’s computer. At this stage, task mining tools collect data like clicks, scrolls, and other actions, including time stamps and screenshots.

Figure 2 is an example illustrating the user activity captured by task mining and plotted as a dataset on cloud.

Figure 2: Task Mining Data Overview

This activity can lead to a breach of users’ privacy, that is why task mining vendors put significant effort into cleaning Personally Identifiable Information (PII) from the data that they collect. Vendors are already building solutions to automatically clean PII from their task related data.

2. Recognize context with Optical Character Recognition (OCR)

While recording, task mining tools leverage OCR technology to understand the context of the task. For that, it collects words, numbers, and other additional text from the recording and the screenshots captured during user activity.

For instance, in the analysis we conducted, task mining tool has provided us with frequent actions and hops our employee executed on different applications (Figure 3).

Figure 3: Task mining recognizes user activities and group them.

3. Group similar activities with Natural Language Processing (NLP)

NLP helps task mining tools understand the context of tasks better and group similar activities. This enables tools to understand particular actions to perform a specific task. For example, activities required for managing purchasing tasks can be identified at this stage.

Task mining seamlessly generates several graphs displaying insights on grouped activities, such as time spent per application (Figure 4).

Figure 4: Task mining group activities and share insights about them.

4. Match user activity groups with business tasks 

After grouping, user activities are matched with specific business tasks. For example, the record of filling in the order form is matched with the task of order form filling. This matching is required to assess performance metrics with the recording.

5. Evaluate performance by using performance metrics

In the end, records are evaluated with business data like KPI measures. This way, companies can see how they perform their tasks and measure how well they perform those tasks. With this assessment, businesses can improve their performance metrics by taking the following actions:

  • Identify why some tasks are performed inefficiently
  • Identify the most common errors and train employees to avoid them
  • Discover automation opportunities

For instance, the task mining we utilized enabled us to assess the overall productivity of our employee through the dashboards it automatically generates after collecting the data.

Figure 5: Productivity dashboard

How is the interest in task mining?

Figure 6: Task mining Google search trends over 10 years, Source: Google Trends

When we look at the last ten years, we still observe moderate interest in task mining, and its popularity hasn’t increased substantially yet. The reasons might be:

  • the increasing popularity of process mining, because most task mining vendors also offer process mining solutions.
  • Task mining with the use of computer vision and NLP, it is emerging technology that we will probably hear more about in the future, and it hasn’t had enough time to become a well-known solution yet.

However, as seen above, Google Trends predicts that the interest in task mining will significantly increase soon.

How does task mining differ from process mining?

Task mining technology works similarly to process mining, and they both aim for improved process efficiency. However, there are slight differences between these two technologies while understanding the processes.

Process mining focuses on performance metrics and the order of process steps to understand how a process is handled. Using log files, it enables businesses to see the actual steps to perform a process and identify the bottlenecks through business data like KPIs for process improvement or discovering automation opportunities.

On the other hand, task mining focuses on the way businesses handle a specific process step by recordings and screenshots. It helps companies understand what employees actually do while doing a particular task and identify the most common actions through user interactions. This data is then used for process improvement.

Task mining shows how businesses manage their tasks more precisely, and process mining enables them to leverage business data to understand how well they perform their processes. We see these as closely related technologies that should be used together to expand their impact and gain a better understanding of how processes are actually performed.

Further Reading

You can read more about process mining technologies from our related articles:

If you want to look for an extensive list, you can also check list of process mining vendors.

If you still have questions about task mining software, we would like to help:

Find the Right Vendors

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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