RPA is commonly perceived as an automation tool, but enterprises are starting to recognize that these same tools can also help automate various analytics processes.
Increasingly, organizations are using robotic process automation in analytics tasks from assembling data spread across the company to analyzing how business processes work and how they can improve.
"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.
When it comes to RPA and analytics, the automation tool should be a complement to, rather than a replacement for, an integrated data platform across the company, said Jonathan Hassell, content director for data and AI at O'Reilly Media. When companies lack an integrated data platform, it makes analytics more difficult overall.
"RPA can help in some ways, but the potential for RPA to unlock insights in data and further output and processes requires a good data platform with great health and hygiene," he said.
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.
"Not only will it tell you what the ice cream is, but it will tell you how to make a smoother, creamier fill," Hassell said. "It can look for deficiencies, inefficiencies in processes and workflows, and help highlight ways to improve those."
To better understand how RPA and analytics work together, here are nine 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
Online businesses have pioneered processes for incrementally testing new UX variations as small experiments and then scaling up the successes. Henning Dransfeld, principal consultant at ISG Research, said RPA can make it easier for businesses to conduct these types of experiments on more complex business offerings, too.
"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.
His team created a proof of concept that used bots to monitor the Airbnb site to analyze the average costs of rental units based on layout. The information is then passed to a machine learning engine, which can identify offers that fall below an average threshold, thus indicating a good deal for the consumer.
According to Easter, the largest challenge is the resiliency of the bots themselves. Websites and other data sources are constantly changing, and bots need the ability to continue running even when layout, colors or icons change on the data source.
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. Exploring analytics proofs of concept
RPA is a great way to automate the collection of data from remote systems where direct data access or API access isn't an option or it isn't an easily implemented option. There are many systems that provide reports and data once logged into the native interface, and reporting and analytics teams take this data and ingest it into data repositories.
"This is especially useful during proof of concept before moving onto more costly and time-consuming extract transform [and] load and data processing," said John Samuel, executive vice president leading digital transformation, IT and cybersecurity at CGS, a global applications, learning and outsourcing services company.
6. 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.
But IT leaders need to watch out when using RPA and analytics to get a few more years out of legacy systems and avoid a costly, time-consuming and error-prone system upgrade. Less obvious costs associated with maintaining the older application include performance, compliance and security issues. These are better addressed by an upgrade that might get pushed off by deploying RPA as an interim solution, Knisley said.
7. Mitigating COVID-19 pressures
RPA can help quickly set up new monitoring capabilities on short notice. For example, during the early onset of the pandemic, the U.K.'s Northampton General Hospital used RPA to automate the monitoring, analysis and reporting of the medical center's oxygen supply. The monitoring of the tanks was a critical manual process, requiring hospital staff to log in to a system and physically collect each reading from the tanks.
"In the face of additional pressure brought on by the pandemic, a new system was required to free up resources and reduce unnecessary risk of error as information was reported," said Stephen DeWitt, chief strategy officer at Automation Anywhere, an RPA tools provider.
The hospital worked with Automation Anywhere to develop the bots that could automate the manual process. The bots also improved workflows for assigning patents to hospital wards to enable an equal distribution of oxygen. Automating the collection and analysis of this data mitigated clinical risk by ensuring demand is properly shared across the oxygen tanks.
8. Analyzing business processes
There is debate about whether to improve a business process first and then automate it or to automate it first with RPA. Wayne Butterfield, director at ISG Research, observed that using RPA on existing processes can generate a nearly limitless treasure trove of data exhaust.
In the long run, this makes it easier to engineer the process. In the short run, this data exhaust provides more sophisticated reporting that combines data pulled from multiple systems touched by the process. For example, the data on invoice processing tasks may have traditionally been limited to the number of invoices processed by each employee. Analyzing the data generated from an automated process makes it easier to derive insight related to who the invoice is from, when was it received, how much it was for and how long it took to pay.
9. Gaining more granular process analytics
"A well-crafted RPA process can also provide details about the process execution that would be difficult, if not impossible, to capture via a similar manual process," said Yaakov Shapiro, CTO at Tangoe, a telecom expense management space.
For example, a process might involve a repetitive task requiring the retrieval of a file, extracting some details from the file and loading them into a data cube. RPA is exceptionally good at mimicking the way a human worker would perform the same action but also capture nuanced information about the process itself. This includes data such as time spent downloading, file size, transmission rate and the success or failure of extraction. These details can further inform the original analytics and help IT teams prioritize improvements, Shapiro said.