What is the difference between Cognitive RPA and BI?
Business intelligence (BI) includes software frameworks (e.g. instruments, apps, best practices, methodologies) that empower corporate managers to make optimized operational choices, create informed policies and tactics, and promote enhanced efficiency. Dating back to the 1980s, this concept relates to trends and perspectives obtained when big information volumes are collected, analyzed and visualized for company decision-makers and end-users from the different operating systems and databases of a company. The Global Business Intelligence (BI) market is anticipated to expand from $15.64 billion in 2016 to $29.48 billion by 2022 with a CAGR of 11.1 percent, according to Statistics MRC. Which means a lot of money is invested in hiring analysts, business intelligence professionals, and BI tools implementation worldwide. The most popular BI tools are Power BI and Tableau from Microsoft and Salesforce accordingly. Both tools require massive training and understanding the data structure for further use.
Some firms or company leaders may wonder why they should invest in business intelligence when they already have a powerful basis within their organization for reporting instruments and descriptive analytics. Business Intelligence relies on a production metric or information source to provide insights into historical developments and the status quo. On the other hand, cognitive automation uses more advanced technologies, such as natural language processing (NLP), text analytics, data mining, semantic technology, and machine learning, to make it easier for the human workforce to make informed business decisions. Cognitive RPA allows for deeper analysis by relying on multiple inputs and uncovering previously unidentified processes from a new angle for non-technical employees. In order to dive deeper for the basic details, all employees should have the technical possibility to get the basic data details, which is related to their everyday job. Continuous Improvement will bring the company new ideas in case all employees start using company data in a more intuitive way, which brings the company new savings.
When instances of leveraging business intelligence first emerged across sectors such as healthcare, hospitality, or consulting, the most prevalent consumers of such apps were people in IT jobs. And company analysts depended on the IT architects and developers of their company to enable access to critical query outcomes and company analyzes. However, BI instruments have become more flexible, user-friendly, and intuitive over the course of time. Business intelligence is now commonly leveraged by both executives and staff in streamlining their daily decision-making processes, partly because of self-service innovations. Self-service innovations are amazing smart instruments implemented by company employees using available tools for automating the process. The only weakness of self-service innovation is support after the self-service innovator changes the company.
However, a contrast to traditional company intelligence instruments and self-service innovation tools that are more widely used with standardized information sets, the recent techniques — such as cognitive robotic process automation (RPA) and AI — go one step further to enable contemporary forms of BI-driven analytics.
You may be asking at this stage what importance a formalized business intelligence initiative might have for your business. From the very start, company executives have used their intuition and fundamental operational indicators to understand their present company state. However, more data can be accessed by more non-technical skilled employees for the decision-making process the greater and better the results be in daily business operations.
Business intelligence practices provide fresh insights in defining operational bottlenecks, company possibilities, and market trends as well as their relationships to individual procedures by drawing significance from quantifiable information on which to base company decisions. Cognitive automation insights for the non-technically skilled employee can show where procedures can be engineered more efficiently and legitimize fresh approaches to advance the business. New Continuous Improvement ideas generated by non-technical employees with additional value for company efficiency and business results.
The main benefit when a business engages with cognitive RPA is the ability to access and leverage data rapidly, regardless of source (e.g. SAP ERP, CRM, SQL/NoSQL database, etc.). At a moment's notice, business managers and operational employees can use extremely intuitive, precise, and extensive data that rivals can only obtain through long-term attempts on their part.
BI instruments must be able to handle the infrastructure of a company (i.e. software used, processes, organizational structure) even more than other software. The greatest challenge here is that for the best gain of understanding, the BI solution must be prepared to access and process preferably all accessible information sources in the business, some of which may still be analogous.
When Cognitive RPA and BI are used to work towards a common goal, information can be put together more readily and make real digital businesses more effectively insightful. On the one side, cognitive automation efforts can empower BI analysts by promoting information digitization and taking over information collection and analysis components that are linked to heavy manual effort, repetition, and standardization. While Cognitive automation and BI can already provide you results when individually leveraged, bringing together the two technologies leads to a whole that is larger than the sum of its parts.