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“Data and analytics leaders responsible for implementing artificial intelligence techniques and digital business transformations should identify desktop-level inefficiencies and task automation opportunities by using task mining tools.” - Gartner, Task Mining Market Guide 2022
With the sheer quantity of tools that can be used for digital transformation, it can be hard to make sense of the market. Task mining is a relatively new technology that has gained momentum in the last few years, spawning startups and leading established players to develop a task mining offering. But what is it?
Task mining analyzes clicks and keystrokes to generate novel insights about how users interact with their computers. These insights include visualizations and analytics that can be used for automation, optimization, and other types of transformation. Any time that knowledge is desired on how work gets done, task mining can be used. While traditional process analysis relies on manual methods that often miss the mark, task mining observes users to show exactly how a process is performed. Advanced task mining tools can generate accurate process maps that contain all variants in a process and highlight recommended areas for process re-engineering or automation.
How task mining works
A key feature of task mining is that data does not need to be in a specific format for the technology to work. In fact, existing data is not necessary at all since task mining collects data by observing employees conduct their work as usual. This means that there is essentially no barrier to entry when it comes to getting started with task mining. Additionally, advanced task mining tools require little to no manual effort on the part of the user to generate insights.
Task mining works by collecting data from the clicks and keystrokes that users perform. This is typically done by applying computer vision to screenshots, querying the operating system, or some combination of both. Task mining then uses machine learning to discover structure in the data it collects and generate insights. In as little as one week, task mining can deliver meaningful results.
Use cases
Process Discovery: task mining identifies the processes that make up an organization. By looking for repetition in day-to-day work, task mining accurately predicts when a process is occurring and where it starts and stops.
Process Definition: task mining generates process maps that show exactly how a process is being performed; advanced tools can show all decision points and variations and provide detail down to the screenshot level. Anonymization techniques can be used to mitigate security concerns regarding PII and any other type of sensitive data.
Process Analysis: task mining uses machine learning to generate insights about worker tasks, including handle time, automatability, and overall process volume.
Applications
In any type of process improvement, the first step is gaining a thorough understanding of the as-is state. No technology is better for this than task mining.
Automation: the number one application of task mining is scaling enterprise automation like RPA or Intelligent Automation. Three key automation challenges that task mining helps with are discovery, speed, and quality.
- Discovery: automation teams struggle to identify the right opportunities and prioritize them based on impact and effort; task mining solves this by automatically identifying automation opportunities and calculating key metrics that help with prioritization
- Speed: mapping processes is a highly manual process and development lead times are long due to inaccurate, incomplete, or hard to use requirements documents; task mining solves this by generating process maps in as little as 1 week
- Quality: processes aren’t as automatable as initially thought or only “happy path” processes are developed, leading to failed automations; task mining shows exactly how a process is performed, including all variants
Analytics and Optimization: task mining guides optimization efforts by calculating machine learning assisted metrics and identifying steps in a process that are ripe for improvement.
- Analytics: average handle time, handle time distribution, time spent per application, structured data percentage, variance, and many other metrics can be calculated using task mining.
- Improvement identification: task mining can identify the non-standardized parts of a process and process bottlenecks, helping teams quickly identify where there are opportunities for process standardization or improvement.
Examples
The beauty of task mining is that it can be used any time people are doing work on a computer. Some common examples include:
- Finance and accounting processes like accounts payable and report creation
- HR processes like onboarding and payroll
- Operations processes like supplier onboarding and back office processing
- IT processes like helpdesk management
- Customer service processes like call center operations and responding to customer enquiries
The above represent just a few examples–task mining is industry and function agnostic.
How to get started
One of the key benefits of task mining is that it does not require extensive internal resources to deploy. With advanced task mining tools like Mimica, virtually no manual work is required by the user to generate insights. For this reason, task mining is much easier and quicker to deploy than similar technologies like process mining that often require entire teams of highly trained specialists to realize value.
Where you start with task mining depends on what your specific priorities are. Whether you need to identify new automation or optimization opportunities, map a known process, or gain insight into a lesser known area of the business, task mining can help. Tools like Mimica that enable improved and automated processes lead to lower costs, more engaged employees, and happier customers.
