“RPA is helping streamline the processes that create valuable insights, changing what areas analytics are measuring and helping to find new domains of time-consuming tasks to focus on,” said Michael Shepherd, engineer at Dell Technologies Services.
Hassell recommended organizations look at three key ways RPA can change analytics. First, it helps create better data from the outset. Second, the organization can deploy it in the context of machine learning to sift through large quantities of data and identify useful information for humans to look at. Third, RPA can help optimize a process that generates data.
To better understand how RPA and analytics work together, here are few ways organizations can utilize the automation software to gain better insights in data.
1. Analyzing across dispersed documents
Shepherd’s team at Dell recently saw an opportunity where RPA could help their financial team. However, while trying to implement end-to-end RPA processes and other offerings, they found that no one could accurately extract information needed from dispersed digital documents.
“This learning process led us to focus on new analytics practices and new domains of computer vision,” Shepherd said. They ended up developing a new technique influenced by RPA that also increased the accuracy within the RPA process.
2. Conducting business experiments
“The demand for analytics in business is growing exponentially as a means of generating insights and reducing business risk from new service models,” he said. Insurance companies launch new digital products, manufacturers launch products as a service and retailers change product price ranges in accordance with the weather or events. RPA can process data from more sources touched by a new offering and perform automatic calculations to make smarter business decisions.
3. Scraping the web for analytics
Organizations can utilize RPA to automatically collect data from websites for a variety of purposes, including analytics and AI. “RPA is commonly used to collect information more broadly and efficiently than collecting such data through manual means,” said David Easter, principal RPA project manager at Micro Focus, a digital transformation consultancy and tools provider.
4. Improving data transparency
Humans are prone to making mistakes when manually processing higher volumes of data. “A major advantage of RPA is to give us higher quality with almost no errors,” said Sunil Kanchi, CIO at UST Global, a digital transformation services provider. The company deployed RPA internally on several business processes and saw dramatic improvements in savings on labor and increased accuracy. In accounts payable alone, it was able to bring in over 80% savings in manpower and transparency in the payments to receivers, while eliminating errors.
5. Automating data wrangling
Analytics professionals spend an inordinate amount of time wrangling to clean, structure and enhance raw data for better decision-making. “RPA excels at automatically delivering clean, structured data ready for analysis,” said Jon Knisley, principal of automation and process excellence at FortressIQ, a process mining tools provider.
The automation technology can provide data faster and more consistently with fewer errors — that way, analysts can avoid the tedious and time-consuming data preparation work, focusing more time on the engaging aspect of their role to uncover beneficial insights.