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RPA vs Process Mining in '24: Differences & Use Cases

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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Businesses are increasing their adoption of RPA and process mining:

  • 93% of business leaders claim to want to deploy process mining in their organizations.
  • Due to COVID, 76% of organizations have adopted automation.

RPA allows for the automation of repetitive operations, which frees up staff time. Process mining can be a de facto X-ray machine, illuminating visibility into the company’s current processes in order to identify areas for development. 

But late adopters must be aware of these fundamentals before embarking on the journey of implementing either or both of these technologies. Lack of insight into:

  • The precise capabilities of each tool, 
  • How they differ from one another, 
  • And how they might work together, could result in unnecessary expenditures and unrealized profits.

In this article, we will explain the difference between RPA and process mining, each tool’s specific use cases, and close by examining how they can be used in tandem with each other. 

What is RPA? 

RPA, or robotic process automation, is a tool businesses can use to automate front and back-end processes. 

RPA bots can be programmed through coding, no-code deployment, drag-&-drop interface, and hybrid automation.

To learn more about the different types of RPA click here

Why is RPA important today?

RPA has recently become important because it can: 

  • Support remote workforce, with the bots undertaking tasks and employees monitoring the bots’ activities via dashboards remotely.
  • Bridge the gaps between different legacy systems through orchestration without needing to leverage APIs. Check our article on the use of RPA in legacy system integration for more.
  • Function on a variety of different virtual, remote operating systems. 
  • Function more efficiently and economically than business outsourcing. 

To learn more about RPA’s importance today, click here.

What are the use cases of RPA?  

RPA’s use cases include, but are not limited to, the following: 

  • Data entry, updates, validation, 
  • Document understanding, 
  • Lead nurturing, 
  • Updating CRM, 
  • Regular diagnostics, 
  • Regulatory compliance,
  • Updating user preferences, 
  • Bank statement reconciliation, 
  • Resume screening, 
  • Employee onboarding, 
  • Travel and expense management, 
  • Validating and processing loan applications, 
  • Bill of Material (BOM) processing, and more. 

To learn more about the use cases of RPA in different business applications, click here. 

The common denominator with most of these tasks is that they are not particularly heavy-duty but time-consuming and mundane, which translates to workers’ time loss, could result in bottlenecks (imagine hundreds of candidates’ resumes getting piled up slows down recruitment), and are prone to errors.  

RPA, by following a pre-determined, rule-based script, will execute each task as it’s been programmed to do so. This minimizes the risk of manual mistakes and completes tasks faster and on a higher scale. 

What is process mining?

Process mining is a process technology that acts as an X-ray, shining a light on the actualities of a process instead of the ideals. It does that by extracting real-time event log data and activity records to show how each specific workflow is carried out from beginning to end. 

Explore more on how process mining works, how it leverages machine learning and top algorithms it employs.

The insight that analysts gain from using process mining to direct their processes can act as an indication of which areas or specific sub-processes need optimization, largely through automation.

Discover how process mining enhances and streamlines:

Why is process mining important today?

Process mining is important because it rids managers of the romanticized versions they might have about their processes and instead presents them with the actualities of how things are. 

For instance, a company might revitalize its Bill of Material (BOM) processing. But the managers might not be aware of the time lapse between checking the inventory level of the intermediate good that goes into the production process and its input on the BOM. It’s through mining this process that the analysts can be provided with data-driven insights into how long each process takes. 

Learn how process mining contribute to process knowledge, process intelligence and operational efficiency.

Having this information can then fuel the automation initiatives because the analysts would know that, perhaps, automating inventory control can first, decrease the time at which inventory levels are established, and second, streamline the transfer of data from the ERP database onto the BOM. 

To explore more about the importance of process mining, click here.

What are the use cases of process mining? 

Companies can run process mining technology on the majority of their front and back-end office processes in order to achieve deeper levels of insights and understanding as to how they operate. 

But specifically, users have claimed to be using process mining for these purposes: 

To learn more about process mining use cases, click here.

There’s one common denominator with all the use cases that users’ have mentioned: they’ve used process mining as a stepping stone for automating their processes. So if the insights gained from process mining are not acted upon, they do not provide much value to enterprises by themselves. We will go over how RPA and process mining should be used in tandem later on. 

To discover process mining real-life examples, click here.

How are RPA and process mining different? 

RPA is a tool for automating processes. Process mining is a process intelligence like task mining or process modeling that provides insights about processes that require improvement, harmonization, or automation.

So one tool is for identifying automation opportunities, while the other is an automation enabler. If companies leverage RPA in processes that they have little knowledge about, the automation initiative might fail because maybe:

  • The wrong process had been automated, 
  • The scale of the automaton project was not enough,
  • The process was not manual-ridden to benefit from the antidote, 
  • The process was too complex with multiple interdependencies, and more. 

We have an article that discusses common RPA pitfalls in more detail.

On the other hand, even if companies do get the most in-depth looks into their processes, it will not count for much if they do not get used as the bedrock upon which automation gets built. That is why it’s important to act upon the gained insights.

How to use RPA and process mining complementarily? 

The insights gained from the process mining tools can shed light on the actualities of the existing processes. Specifically, it can lay bare: 

  • Process variations, durations, and other log events,
  • Pain points and bottlenecks, 
  • The extent of the process’ maturity (i.e. how many rounds of structural changes has it gone through within a specific timespan?)
  • And the extent of manual involvement in it. 

Moreover, businesses can, through process mining’s DTO (digital twin of an organization) feature that leading vendors offer, create a counterfactual scenario for their processes to get a glimpse at the optimized version of their current workflow. 

For example, a DTO of the claims management process for an insurance company might show that data extraction from documents, such as written witness statements and medical reports, can be done at a faster rate. The CoE or automation consultants can then suggest the insurance company leverage IA-enabled RPA bots that benefit from OCR functionality to read documents and extract information from them at a faster rate. 

Sponsored:

IBM Cloud Pak for Business Automation offers process mining and task mining solutions integrated with the digital twin of an organization (DTO) technology and Automatic Bot Generation capabilities to streamline the process automation journey of businesses.

IBM clients can automatically extract and analyze process data and look for processes that require automation. With these critical capabilities, IBM process mining users can simulate the possible scenarios for automation together with the expected cost and ROI details by setting the automation level and RPA complexity. Business leaders and analysts can plan their RPA implementation projects based on these data-driven insights before investments are carried out. They can automatically generate RPA scripts by utilizing user workforce insights recorded during the analysis.

If companies can use process technology such as process mining or automated process discovery to extract information as to how they can improve their workflows, and act upon those insights by leveraging automation technologies, they can enhance the efficiency of their processes. 

To learn more on how you can deploy RPA with process mining, click here.

For more on RPA and process mining

If you believe your business would benefit from adopting RPA and process mining technology, head over to our:

Where you’ll find data-driven lists of vendors. We can help you choose amongst them:

Find the Right Vendors

If you want a more in-depth look into RPA, download our whitepaper on the topic:

Get RPA Whitepaper
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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