Despite the claims, Robotic Process Automation’s benefits to analytics are limited

Despite the claims, Robotic Process Automation’s benefits to analytics are limited

We had explained RPA extensively in layman’s terms and outlined its benefits. One of the frequently cited benefits is improved analytics and big data analytics has been a priority for executives for the past decade. So how does RPA benefit analytics?

Explore how RPA contributes to analytics

We should consider the analytics funnel above to see where RPA can contribute. Bots have essentially 2 critical functions from a data standpoint:

  1. Create meta data: As they complete tasks, they record their progress and the issues they face for diagnostic purposes. This data can be used for both the client or the RPA provider to identify RPA bugs and improve bot performance.
  2. Enable access to data in legacy systems: Since they overtake tasks that require interfacing with legacy systems, they make previously difficult to access data accessible. This can transform data collection capabilities of enterprises, especially those that depend on legacy systems.
As you can see, bots do not essentially improve analytics capabilities but aid in data collection. Even RPA vendors agree with this, underlining that core benefit of RPA is in data federation: the capability to collect data from many different sources and aggregate it in an easy-to-analyze format.

Explore how data federation can contribute to your company’s performance

Firstly, data federation should not be a major concern for an SME or startup. However for large companies, it is a major concern as legacy systems historically held large companies back in terms of easy access to data. Now with access to granular data about processes, large companies can reap 2 important benefits:

Process optimization thanks to process mining

Granular data about processes can help identify bottlenecks and inefficiencies, enabling corporations to increase both speed and efficiency of the process. Furthermore, it makes dissemination of best practices easier. Since process flows can easily be visualized with the help of data, process flows in different regions can be compared to find the best processes for the whole company.

For complex inter-related processes, machine learning techniques could be used to find optimizations that analysts could easily miss. Here are some examples from PwC

Machine learning might come up with the suggestion that ordering material X from supplier A in the week of Christmas instead of the first week in January will result in a 50% improvement in order fulfilment in January. You could change the RPA robot setting in line with this suggestion to make sure orders to the relevant suppliers are placed during Christmas week, while your staff are on vacation. Apart from its ability to generate simple correlations, machine learning combined with today’s computing power is increasingly capable of identifying unknown relationships within multiple business processes. For instance, it can potentially correlate procumbent processes with sales processes to analyse directly what supply chain management actions need to be taken to improve sales

Process simulation

Some major decisions like outsourcing, workforce reductions or expansions are made haphazardly, based on urgencies without considering future implications. Such decisions tend to have long-lasting impact because once a process is outsourced or its headcount increased, it is difficult to roll-back such decisions due to risk averseness inherent in humans. Managers, especially those in well performing companies, would like to see how rolling back such changes will not impact operations. Process simulation provides an answer!
By simulating how a process flow will be impacted by changes, analysts can show the impact of major changes on the process. RPA systems can provide the necessary data for such simulations.
RPA can have other fundamental benefits for cost management and elimination of manual errors as we outlined in our comprehensive list of RPA benefits.


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