Robotic Process Automation (RPA) bots that perform repetitive tasks could be useful for enterprises looking to automate data management tasks, from data entry and cleansing to document recognition and metadata management.
RPA is growing in demand as more companies recognize its benefit for management in big data and artificial intelligence (AI). RPA refers to programs imitating processes with data that are typically done manually. These scripts create robotic actions that automatically perform rule-based assignments with data, generally of a repetitive nature. They are, in essence, software robots.
How RPA can help Data Management
There are numerous new possibilities to grow for enterprises that emerge for employe RPA in data management. Data management includes various repetitive tasks in collection and curation that can benefit from automation. Applying RPA to extensive data repositories makes tasks such as data entry, capture, and creation or updating of metadata much more efficient. All of these assignments are highly repetitive and also tend to be unique.
Each data access situation demands special considerations. This provides an ideal opportunity for applying RPA.
RPA can be combined with other techniques to create complex data managing solutions. One example is the use of RPA to extract information from scanned documents using Optical Character Recognition (OCR) to create metadata and reduce content to a usable format for big data or machine learning (ML) processes.
Benefits from implementing ElectroNeek RPA
- Data entry, replacing hand-operated keying or document submission
- The ability to connect any external source through the code
- Fixed pricing with no limits on the volume of processed data
- Repetition rate of data update requests from 0.1 second
- Presets for MS Excel and Google SpreadSheets
- OCR recognizes data from PDF and scans
- The ability to set up working hours and schedules
- Installation on your server or the cloud (security)
- Connectors to FTP, SQL, MongoDB and other databases
- Works with popular ETL infrastructures (Amazon Redshift, BigQuery, Snowflake)
This list will expand as RPA develops further. As the amount and types of data grow, RPA provides exceptional solutions for improving outcomes in all analytics areas. Augmented by machine learning and AI, RPA can streamline input (such as initial processing of images and documents) and improve processes through decreasing time spent on these tasks. RPA can reduce headcounts for manual and highly repetitive processes, and it can be used to enhance the quality of data.
If you want to learn more about the use of ElectroNeek RPA in Data Management, reach out to our automation experts.