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4 Takeaways from Deloitte’s State of Process Mining 2021 Report

4 Takeaways from Deloitte’s State of Process Mining 2021 Report

Deloitte has just released their 2021 Global Process Mining Survey. The survey features some interesting insights and for anyone in the process mining, process excellence, or automation space, the 36 page report is definitely worth a read.

Based on a survey of companies that have “already adopted Process Mining or those that are looking to start their first Process Mining initiatives”, the report presents several data points on the use cases, expectations, and challenges of process mining being experienced by organizations that have already evaluated or made the decision to move forward with implementing the technology.

Overall, the report paints the picture of a technology gaining traction across industries and use cases. That being said, it also presents evidence that reinforces some of the common pitfalls of traditional log based process mining techniques.

Below are four of our top takeaways from the report:

  1. Most organizations see value from process mining, but that value cannot always be tied to direct outcomes or ROI

While the report states “84 percent of organizations responded that they believed process mining delivers value”, the value was not always “direct value” that can be tied to tangible return on investment. In fact, the top form of value identified in the report was simply developing transparency of as-is processes.

Organizations had far less expectations for process mining when it came to taking action on that visibility. Commonly cited outcomes such as “Automation” and “Process Redesign” were far down the list of the top expectations (5th and 7th respectively), and both were cited by less than 50% of respondents as an expected outcome of process mining.

In Skan’s view, this reflects a somber realization by enterprises that traditional process mining approaches that focuses strictly on back end logs do not provide a complete, end to end view of processes, and have not lead to success in automation and process redesign efforts. Skan is seeing more and more customers start to look beyond these outdated process mining techniques, prioritizing instead technologies (such as computer vision) that provide visibility of front end tasks within processes to develop a complete, end-to-end view of their processes.

 

  1. Traditional back-end processes continue to the most common use cases, but front office use cases are gaining traction

The back end processes that have been traditional use cases for both process mining and automation continued to top the list of areas utilizing process mining. “Purchase/Procurement” and “Accounting/Payment”, were the 1st and 2nd most utilized areas. Logistics was reported as the 5th most utilized area.

However, several customer facing front office areas also appeared fairly high on the list, including Sales (3rd) and Customer Service (7th).

  1. The paradox of traditional process mining: you need process knowledge to get process knowledge

The highest responded “critical skill” for the success of a process mining implementation was “Process Knowledge”. Additionally, the second most reported critical success factor for process mining was ‘good data quality’.

It seems somewhat counterintuitive that the most critical success factors for getting value from a technology that is supposed to deliver process data and knowledge is existing process data and knowledge.

 

  1. The problem and need being solved by process mining is here to stay

Deloitte expects big growth for Process Mining - 70% CAGR over the next 5 years. This will likely come from existing users of process mining – the report states 83% of organizations using process mining will continue or expand their use of the technology – as well as adoption across new organizations and industries.

 

Our summary: Enterprises are seeing real value from a deeper understanding of their processes. However, the process mining category needs to and will continue to evolve beyond simply mining process information to providing actionable intelligence. Enterprises need to a full view of their processes, not just what is visible in back-end logs. Only then can they decide on meaningful action, and quantitatively measure the results of those decision to determine true ROI.

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