UIPath Process Mining vs AllActivity Task Mining
As more and more organizations seek to embrace Intelligent Automation at an enterprise scale, many are coming to the realize that they also need to standardize and optimize their processes to fully unlock all the available benefits of being efficient and productive.
There is a lot of confusion when it comes to process mining and task mining as most of the process mining vendors provide (or say to do so) task mining services as well, and they have been using these words interchangeably. Also, when it comes to the final outcome, both these mining technologies complement each other and helps organizations to be optimized and perform better.
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While Process Mining is a powerful tool for gaining insights on enterprise level processes, Task Mining operates at a desktop level to discover and analyses the tasks that users perform in between taking part in enterprise level processes (As UiPath Process Mining does).
This brings into focus opportunities for automation that would be missed if using Process Mining alone. Many of the automation opportunities highlighted by Task Mining are good candidates for citizen developers, being by their nature shorter and less complex than those captured through process mining software. Now let’s see why it is better than process mining:
Combined, process mining and task mining solutions set up your business for success! Both approaches have their own space and distinct capabilities, as seen above, but it is important that task mining has more updated issues in workplace to the concern of privacy & personal space if we compare it to process mining. And, in the process of conduction, there are much more complexities in process mining than task mining.
The expectations of data analytics and data mining findings vary because both have different responsibilities. While data mining is responsible for discovering and extracting patterns and structure within the data, data analytics develops models and tests the hypothesis using analytical methods.
Data mining, with the right software, can collect the data ready for further analysis. At this stage, a larger team simply isn’t required. From here, a data mining specialist will usually report their findings to the client, leaving the next steps in someone else’s hands.
In contrast, when it comes to data analytics, a team of specialists may be needed. They need to assess the data, figure out patterns, and draw conclusions. They may use machine learning or prognostication analytics to help with the processing, but this still has a human element involved.
However, data analytics involves collecting data and checking for data quality. Typically, a data analytics team member will be working with good quality raw data that is as clean as possible. When the data quality is poor, it can negatively impact the results, even if the process is the same as with clean data. This is a vital step in data analytics, so the team must check that the data quality is good enough to start with.
Two essential aspects to take into account are:
- Data structure. When it comes to data mining, studies are conducted mostly on structured data. A specialist will use data analysis programs to research and mine data. They report their findings to the client through graphs and spreadsheets. This is often a very visual explanation, due to the complicated nature of the data. Clients are not typically data mining experts, and they don’t claim to be.
So, data needs to be simply interpreted into graphics or bar charts. As with the earlier phone company example, if the client needs to know the data behind how many people click the link to ‘what is a VoIP number’ on their website, this should be displayed in easy-to-read charts, not complicated documents.
Data quality. The way that the data needs to be presented for data mining compared to data analytics varies. While data mining is used to collect data and search for patterns, data analytics tests a hypothesis and translates findings into accessible information. This means the quality of data they work with can differ.
Process mining techniques rely on the availability of event logs, where events have a certain granularity that is deemed appropriate for representing business activities. In this paper, we discuss why choosing a proper granularity level during pre-processing can be challenging and reflect on the implications that such a “fixed” view over the process bears for the analysis. Then, inspired by use cases in the context of user behavior analysis, we envision possible solutions that allow exploring and mining multiple granularity levels of process activities.
Usually, the granularity level of the activities in an event log is fixated during pre-processing with the help of event abstraction techniques. Regarding activity granularity, the process mining literature has mainly focused on the problem of event abstraction, which has been tackled from two angles.
How to best tackle the introduced challenges and the implications is still not clear. Currently, we are thinking about two possible directions, envisioning a scenario where the choice of the granularity level of activities is deferred from the traditional pre-processing phase to the analysis, where users can explore multiple granularity levels and select the desired one interactively.
The broad definition of bottom-up management is it’s a structure where the whole organization participates in the process of leading the organization. This collaborative method gives employees a say in how to accomplish the overall goals and objectives of a business.
The bottom-up approach seeks to identify the master data objects that are already in use across the organization. This approach evaluates the enterprise data assets to locate applications using what are, in effect, replicas of master data objects and then enable their resolution into a proposed master data environment This is more of a widespread assessment of the data sets performed using data discovery tools and documenting the discoveries within a metadata management framework. This process incorporates these stages:
Process mining offers enormous advantages over manual approaches to process analysis, but it has its limitations regarding privacy & personal space. For example:
So, from the above point of view, it can be taken under consideration that task mining is better than process mining in all cases.
Enterprise architecture and technology innovation leaders need to distinguish between process and task mining (different but complementary disciplines) to improve the success of mining initiatives.
We can distinguish between process mining and task mining based on the scope, use case, and goals. Task mining builds a much more detailed view of a process compared with process mining, which is limited to observing only what happens from process to process while task mining allows you to know each process during and between all processes.
Task mining provides Log data in its most granular way while process mining tools examine the underlying process data embedded in business applications.
Now we have arrived to the point where (if not already) it will be clear why Allactivity is your best choice when choosing the right software for your company!
We will focus on the 3 main aspects where Allactivity outstands its competitors: Privacy, Personal space, Actionable Insights.
Allactivity Task Mining beats UiPath Process Mining at all levels. We take the golden egg out of the noise that you find in large amounts of data and translate it to you not only for you to understand it but also providing valuable actionable insights that work as a solution for inefficiencies.
We do not identify ourselves as a tool for old-fashioned hierarchy, as we firmly believe the right way to solve problems is implementing bottom-up processes where the whole team improves as a whole.
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- If you’d like to learn how automation and task mining can take your team or organization to the next level, go to our blog or visit our Task Mining section, where you can find our lifestyle calculator and find out how to be more efficient!
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