Task mining vs. process mining vs. data mining – how do these activities fit into the modern business?
Below, will look at these terms, as well as a set of other concepts related to performance improvement.
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Task Mining to RPA: A Digital Transformation Dictionary
These terms all involve the collection of data, but their usages are slightly different. They do, however, tend to focus on business activities related to process improvement.
Let’s look at the definition of these terms, plus a few other related terms.
Task mining. Task mining refers to the collection of front end user interactions, such as employees’ interaction with software. This data is useful for analyzing employee performance at the level of the workflow.
Process mining. Process mining refers to the collection of back end data from process logs and other data sources. The data collected is typically used to gain insights into the efficiency of business processes. Then, using some of the other tools discussed below, that information Ken help improve those processes.
Data mining. Data mining is a term used in the world of data science. It means extracting data from large datasets, such as web sites. That data is then passed along the data pipeline for further processing.
Process discovery. Process discovery is often used synonymously with task mining. The aim is to extract and analyzed data from software analytics, then use that to understand and improve processes.
Business process management. Business process management, or BPM, is a business discipline dedicated to managing and improving business processes. This discipline will use a variety of techniques and tools to accomplish its aim, including some of those covered here.
Business process improvement. Business process improvement refers to a specific set of techniques within business process management. Unsurprisingly, the aim is to improve business processes incrementally.
Business process modeling. Business process modeling is the practice of graphically representing business processes. Diagrams, flowcharts, and other visual representations make it easier to grasp the components of a process. That understanding can then help business process managers improve those processes.
Continual improvement. Continual improvement, or kaizen, is a concept derived from Japanese management practices. The idea is to gradually and continually enhanced processes over a long period of time, rather than quickly transforming them.
Business process analysis. Business process analysis aims to understand the strengths and weaknesses of existing business processes. It is both a role and a discipline within the organization, and business process analysts will utilize many of the other concepts and tools covered here.
Business transformation. Business transformation refers to a more comprehensive type of organizational change. Process improvement is gradual and incremental, but not transformational. Transformation, on the other hand, is transformational and can take place either slowly or quickly.
Digital process automation. Digital process automation is the process of using digital tools to automate workflows and tasks. Automation and the other concepts covered here go hand in hand – for instance, automation improves process efficiency, relies upon process mining, and drive business transformation.
Workflow automation. Workflow automation means using digital tools to automate specific day-to-day workflows. For example, digital adoption platforms (DAPs) can be used to automate each step in a digital workflow. This can improve process efficiency and free up employee time for other activities.
Robotic process automation (RPA). RPA uses software robots too automate business processes, usually manual workflows. Like other types of automation, RPA works in concert with process mining, task mining, and other process improvement techniques.
Hyperautomation. While RPA refers to the automation of low-level tasks, hyperautomation refers to the automation of cognitive activities and complex business processes. This type of automation involves the use of artificial intelligence, machine learning, and complex technology solutions.
Business intelligence (BI). BI analyzes business data to gain insights into the organization’s health, process performance, and operational efficiency. This data is then used to inform strategic decisions.
Digitization. Digitization refers to the process of making something digital, such as a paper record. It can also refer to changing analog processes to digital processes, such as moving from fax to email.
Digital transformation. Digital transformation includes digitization, but it takes the process one step further. It also includes the transformation of processes, business strategies, operations, and so forth.
All of the concepts covered here are tools in the toolbox that are used by business analysts, business process managers, executives, and anyone else interested in improving organizational performance.
Putting the Pieces Together
Tasks are the building blocks of workflows, which are the building blocks of processes, which are the building blocks of a business.
Any organization intent on transformation, either gradual and incremental or radical, will need to examine every aspect of the business, from top to bottom.
This means assessing strategy as well as the resources utilized at the process and workflow levels.Tools and techniques such as those covered above include many of the key concepts related to digital business transformation. Professionals interested in improving their organization’s digital maturity should start by researching some of the concepts described here.