Turning big data into big content: business process management is resurging with robotics process automation

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A delicate shift is occurring throughout the data landscape, with consequences that are wide-sweeping but also subtle. Big data—once deemed essential to capitalizing on data-driven processes—is now viewed only as useful as the meaningful content produced from it. This renewed emphasis on content services prioritizes individual workflows, business process management (BPM), and discreet, repeatable steps for attaining departmental objectives faster (and cheaper) than before. 

Implicit to these goals is the multifaceted utility of robotics process automation (RPA) and virtual agents to complete big data’s reconfiguration into enterprise-friendly, consumable big content. 

“If you think of the workflows that you have in place, in that continuum somewhere there’s an opportunity for a bot,” reflected Ed McQuiston, EVP and chief commercial officer at Hyland. “Somebody who’s going out and researching a website again and again, filling out particular information, can be replaced with a digital worker, allowing knowledge workers to work on truly knowledge-based tasks.” 

Whether deployed on unstructured text, images, or other data types, digital agents are becoming the most effective means of translating the rigors of big data into meaningful content. Their viability for doing so is centered on the following three capabilities: 

♦ AI: Virtual agents are emerging as the de facto means of interacting with both the statistical and knowledgebase side of AI. Bots are either directly empowered with these technologies or access them via services to increase “the democratization of AI,” maintained Abhijit Kakhandiki, senior vice president of products and engineering at Automation Anywhere.

♦ Decision making: Formerly, digital agents only had limited script-based functionality. According to Ramesh Mahalingam, CEO of Vizru, they now possess “that autonomous intelligence, autonomous decisioning capability within each bot that can intelligently change its journey in a conversation based on your answers.” Contemporary bots can render complex decisions such as insurance claims adjudication. 

♦ Self-improvement: The static rigidity of previous digital agents has been replaced by their newfound adaptability to improve over time, so bots actually learn from the actions of—and interactions with—humans. 

These cardinal attributes enable digital agents to shift big data’s focus from sprawling to targeted use cases, accelerate business processes with RPA, and substantially reduce the barrier for leveraging AI while expanding its accessibility. They’re the most viable means of identifying “where there are opportunities to automate and let technology do the heavy lifting in areas where there are interactions and in areas where decisions can be made,” commented Cheryl McKinnon, principal analyst at Forrester. 

Workflow optimization 

There are sundry advantages for leveraging bots to automate business processes while making cumbersome big data useful. The most salient include the following: 

♦ Eliminating human error: The perceived infallibility of bots for rote tasks is well-deserved. Whereas humans rely largely on their visual senses to complete such jobs (such as correctly selecting an item from a drop-down menu), “the bot sees three levels deeper,” Kakhandiki observed. It knows the system ID, or the unique ID of the object that it is supposed to click on. “It will never click on the wrong button.” 

♦ Decoupling tasks: Virtual agents are particularly effective for decoupling lengthy tasks, such as onboarding new customers, into an individual series of steps assigned to different bots. This approach lets organizations “break a large process into smaller consumable bots, place autonomous intelligence within, and leave microservices outside in order for the bot to move the process to the next step,” Mahalingam mentioned. Indico CEO Tom Wilde indicated that with this paradigm, “bots are taught specific tasks that emulate what people do in these workflows.” When processing emails for health insurance claims, for example, separate bots classify the email’s intent, extract content from its body, parse attachments for relevant information, and sequentially use this information to complete claims forms. 

Optimizing resources and scalability: As McQuiston observed, digital agents help knowledge workers to concentrate on more meaningful tasks than repetitive processes, allowing organizations to maximize both technological and human resources. When intelligent technologies such as machine learning or natural language processing are involved, automation tasks become “massively scalable,” noted Rick Hamrick, senior manager of sales engineers and solutions architects at Hyland. 

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