When used in synergy, RPA and data science can provide a host of actionable insights. By the very nature of an RPA (robotic process automation) dictates that large volumes of data will need to be analysed at any given time. Data science is capable of fulfilling this requirement so that stakeholders are capable of making clear decisions at the appropriate times. Let’s take a closer look at the relationship between these two concepts.
Data science in automation is associated with numerous intentions and therefore, it employs many unique approaches. Some examples include specialised programming, advanced algorithms, data-driven analytics (more on this later), machine learning, the collation of big data, and even the presence of artificial intelligence.
Of course, many of these processes are fully automated in nature. For example, a bot can be used to extract and interpret large amounts of data. This information can then be analysed with the help of data science in order to appreciate the “big picture”.
Companies will often need to appreciate future trends before making important strategic decisions. This can be accomplished by examining historical in order to create accurate future predictions.
Sales forecasts, market movements, the potential expansion into a new demographic, and competitor analyses are all impacted by predictive analyses. Data analytics and RPA therefore share a number of inherent traits in common.
Data science automation tools are likewise crucial when referring to CRM (customer relationship management). These approaches can be used to interpret several metrics such as:
Buying habits.
The most popular products.
Client retention rates.
The stages of a sales pipeline.
Which marketing campaigns have proven to be the most effective.
The end result is the ability to create targeted methods to interact with a specific audience and therefore, to implement more effective communications (ultimately leading to brand loyalty and a higher return on investment).
RPA and data science also share a unique relationship in regard to their decidedly technical nature. However, advanced systems now employ software that does not require users to become mired within inordinate amounts of lengthy (and complicated) code.
Pre-designed templates alongside drag-and-drop interfaces now streamline many processes that might have otherwise required a great deal of prior knowledge. One example involves the ability to categorise email campaigns in accordance with factors such as previous buying habits, location, gender, age, and even the device used.
Data science and RPA will likewise involve process mining. Process mining is essentially a series of algorithms that are used to extract event logs and to present this information in a centralised manner. The primary goal is to identify potential issues such as bottlenecks and similar issues that might impact in-house efficiency (particular concerns in relation to robotic process automation).
Machine learning is yet another facet associated with data science and its presence is yet to fully emerge. Machine learning can be used to increase the efficiency and throughput of various tasks such as:
We can now clearly see why utilising RPA and data science in conjunction will yield extremely powerful results. If you wish to enhance the levels of automation within your workplace or to obtain the data-driven result essential before making important strategic decisions, the team at Nugawi Automation is here to help. Contact us to learn how data science is continuing to transform RPA and the business sector as a whole.
Book a free assessment and we’ll prepare an pre-audit of your business to help you automate your tasks.
Nugawi Automation
We help clients to free the human capital trapped in manual and repetitive tasks, leaving them with a durable internal automation adapted to the needs of their company.
