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