Are you tired of spending hours every week compiling data and preparing reports? If so, you may be interested in automating your analytic process with RPA. RPA reporting can help you automate report generation, allowing you to spend less time on administrative tasks and more time on analysis.
In this blog post, we will highlight two crucial RPA use cases in the report, including Data Entry, Integration, Migration, and Data Analytics. We hope that this information will help you decide if RPA is a tool worth considering for your analytics work.
RPA for Data Entry, Integration, and Migration
Due to a lack of system integration, business users must manually enter data from documents such as invoices into one system before rekeying the same data into another. Users have also had to manually look through data in analytics systems to find address fields, postal or ZIP codes, or names that are either missing or duplicated by other inputs.
Therefore, manual data cleansing and record deduplication can be time-consuming and error-prone.
RPA reporting can be used to establish data lakes for data scientists to build sophisticated Machine Learning models, as well as to create and manage well-structured and accurately categorized data across business systems. The RPA software robots can operate through a single or multiple software programs.
The following are some ways that RPA can assist firms in this data cleansing and analytics situation:
- Fully automated data entry – You may completely automate data entry, doing away with manual file sending and keying.
- Automated data migration – Data migration between various enterprise applications is automated, for example, system migrations during mergers or acquisitions.
- Automated data monitoring – Without human intervention, RPA can continuously check for data errors and enhance data consistency.
- Automated data deduplication and retrieval of new data streams – Data deduplication and retrieval of new data streams, such as IoT computer logs and other system-generated data, are automated.
RPA for Data Analytics
Aside from transporting data across enterprise systems, RPA is well-known for being an effective tool for data aggregation, which may be used to improve the supply of data sources for sophisticated processing algorithms. It is boosting data analytics and enabling machine learning, both of which can be utilized to further automate company operations.
Analyzing this RPA-generated data with modern data analytics software will help you gain a deeper understanding of the organization’s processes and workflows, model process improvements, and pinpoint precise process improvement opportunities.
RPA data can then be exposed to numerous data analysis types to further enhance these operations. Here are a few examples of RPA-generated data use cases:
Using machine learning models, we can determine which factors have a significant impact on a process and to what extent. If you feed RPA reporting process audit trails into various machine learning algorithms, you will get prescriptive solutions to improve the processes.
Process Mining tools can be used to visualize the entire process using RPA data, allowing for a far more in-depth understanding of the process and vice versa. Process Mining apps can generate data to choose the best processes for RPA development.
Determining the effects of even small improvements in complex and repetitive business processes can be tough. To specify process requirements and model the accurate outcomes of various situations in automated systems, simulation programs can be easily executed by using process data.
In this extent of this post, hopefully, we’ve given you a good introduction to the possibilities and shown you how RPA can save you time and energy in your analytic reporting process. If you have any questions about RPA reporting or want help getting started with automating your reports, please contact us. We would be happy to walk you through the process step by step!