How to use Big Data and RPA to reduce maintenance costs
Companies are generating and handling more and more volumes of data, so one of the main trends in organizations is the adoption of a ‘data driven’ culture, implementing technologies such as Big Data. In fact, according to data from the National Observatory of Technology and Society (ONTSI), the percentage of Spanish companies with ten or more employees that are already using this technology has grown to 13.9%, 2.8 higher than in the previous year, a figure that rises to 34.7% for large companies.
The purpose of using this data analysis tool is to improve strategic business decision making, which is why taking care of the data collection process and maximizing its quality has become a priority. Therefore, there are formulas oriented towards this goal, like the combination of Big Data with other technologies such as robotic process automation (RPA), allowing companies to automate repetitive and manual tasks, reduce errors and improve data accuracy, as well as increasing efficiency and reducing costs.
In fact, according to knowmad mood, a leading technology consultancy in digital transformation solutions for companies, this combination makes it possible to extract all the advantages of data and thereby reduce maintenance costs by eliminating tools that are no longer needed, as well as increasing productivity. In this regard, the company highlights some more ways that combining these two technologies has a positive impact on the company.
Increased efficiency in data collection, storage and analysis
According to a study conducted by Deloitte, 95% of the organizations that have implemented RPA technology have seen their productivity increased. The reason is due to its great automation capacity, resulting in greater efficiency in the collection of data for Big Data systems. So, once collected, the data is stored in the Big Data systems and, from then on, RPA uses advanced analysis techniques to process the data, identifying patterns and trends.
Improve decision making with more accurate and complete information
Once the information has been processed, it is used to automate the company’s decision-making process. This process is called ‘process mining’ and makes it possible to identify, through the use of data, those tasks that can be automated by RPA, resulting in improved efficiency and decision-making. In this sense, it also makes it possible to increase the speed of innovation by allowing access to a large amount of data.
Reduce costs by automation of repetitive tasks
RPA simulates human interaction with applications and systems through “bots”, which have the ability to automate repetitive and manual tasks, which on the one hand saves a great deal of time, but also saves on the economic resources that the company has and, therefore, can focus on more strategic activities that bring greater value to the company.
Optimize the customer experience with a more efficient service
The combination of RPA and Big Data in the process of collecting and analyzing customer data makes it possible to know and study first-hand their particular needs and preferences, enabling them to personalize their experience and, to offer better products and services that are capable of satisfying their customers’ needs more efficiently. In addition, thanks to the ability to automate tasks, customer service can also be automated. If Conversational AI is also included, this enables companies to deliver their service faster and more accurately. This results in increased customer satisfaction and loyalty.
“In an ever-changing world, a highly volatile market and a business environment where the customer becomes the center of the business strategy, it is necessary for companies to look for formulas that allow them to both gain competitive advantages and improve their results. To this end, the integration of RPA together with Big Data is undoubtedly a powerful combination that we have seen already bring success to many companies in different industries, such as the retail or healthcare sector” says Francisco León Martorell, head of the Big Data Community at knowmad mood.