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State of RPA vs RDA in 2024: 4 Main Differences

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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People might confuse RPA with RDA – perhaps that’s why “RPA vs RDA” is one of the most searched queries on Google (Figure 1).

Screenshot of the results that show up when searching RPA. RPA vs RDA is one of the common results.
Figure 1: RPA vs. RDA is one of the commonly searched queries.

And while these two automation tools have similar-sounding names, and share certain similar properties, they are not the same thing. 

RPA is used for automating a host of repetitive, rules-based tasks with limited input from a human-in-the-loop. RDA, also called robotic desktop automation, is a more primitive, less evolved version of RPA: It is mostly used in automating “simple” desktop tasks for one user only. 

In this article we will explain the differences between RPA and RDA in more detail.

What is RPA?

RPA, also called robotic process automation, is a software technology that automates rules-based tasks in front-office and back-office automation

There are different RPA types in terms of cognition, automation, and human usage.

The use cases of RPA are numerous. Almost any industry that deals with common administrative tasks, such as report preparation, expense management, and the like, can benefit from leveraging RPA. 

What is RDA?

RDA, also called robotic desktop automation, is a technology similar to RPA, but in a smaller scope and scale.

The use cases of RDA are similar to those of RPA. And in terms of benefits, RDA, same as RPA: 

  • Eliminates the possibility of manual errors, 
  • Increases execution speed, 
  • And frees up the staff’s time so they can focus on more value-driven tasks.

What is the difference between RPA and RDA?

The difference between RPA vs RDA lies in the following factors:

1. RPA vs RDA: Human usage 

To understand the difference between RPA vs RDA, we should start with understanding what attended and unattended automation is. 

  • Attended automation: These are automations that should be triggered, monitored, and stopped by humans at specific time intervals.
    • For instance, an HR employee might want to extract the information of a specific employee. He/she can start the data extraction workflow manually, for the bot to automatically carry out its task.
  • Unattended automation: These are automations that start on their own and without human interference.
    • For instance, a marketing manager might schedule an RPA bot to send out a weekly newsletter every Friday, at 1PM. Once the scheduling is set, the RPA bot will send the newsletter consistently at the same time, unless stopped, on a headless operation.

2. RPA vs RDA: Automation scope

While RPA bots aren’t intelligent, their functionalities can be enhanced via intelligent automation, also called hyperautomation

OCR, NLP, advanced analytics, process mining, ICR, computer vision, and other AI technologies can augment the RPA tool for near end-to-end automation of a process. 

For instance, a customer service rep might start a workflow for tending to customer complaints:  

  • OCR will read through the submission and extract the suggestion, the customer’s name, and the date
  • NLP will read through the suggestion to categorize and prioritize it 
  • RPA will put all the data into a machine-readable format 

The point is that RPA can cofunction alongside AI technologies to automate more steps along a process. 

RDA bots, on the other hand, are limited to automating simple, yet time-consuming tasks. So you cannot use RDA to categorize and sort customer complaints. But you can use RDA for invoice validation against a set of predetermined rules. 

The scope of automation (i.e., to what extent can a bot automate a process) is another difference between RPA vs RDA.

3. RPA vs RDA: Remote accessibility

RPA solutions can be installed on the cloud (as RPA as a service), on premises, or on virtual desktops. This means the user can access the solution remotely in order to trigger, schedule, monitor, and stop workflows from anywhere. 

RDA bots; however, can only be installed on individual user desktops. This also limits where the user can access the RDA from. 

For instance, during the COVID shutdown, a purchasing manager could have monitored his/her company’s orders from suppliers via the supply chain automation software from his/her living room. But an accountant who had to reconcile balance sheets, could not do his/her job from his laptop at home, because the RDA solution was installed on his/her work computer in the office. 

So a difference between RPA vs RDA is that RPA is accessible from anywhere, while RDA is only accessible from the desktop.

4. RPA vs RDA: Process security

Both RPA and RDA are suitable for automating rules-based tasks. However, one of the differences between RPA vs RDA is that because RPA is unattended and RDA is attended, the two tools function in different environments. 

The use cases for unattended automation can be reserved to areas where security certification is required. The user would give their username/password to the bot, and the bot would use the credentials to get into the system and do its job. Especially for finance-related tasks, or data extraction from documents, where confidentiality is important, having a bot do the job can be safer. 

Attended automation is best for use cases where the task at hand is fragmented, isolated, and singular. So the bot would, if needed, log into the system with a human permission, do the job, and then log out. And the loop would be repeated every time it’s needed.  

To learn more about RPA

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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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