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How to Leverage RPA for Data Analytics

Robotic Process Automation (RPA) has transitioned from emergent technology to a must-have digital transformation tool for enterprises worldwide, with 93% of organizations surveyed by The Economist agreeing that automation kickstarts digital transformation. Currently, RPA is being used by a growing number of businesses to simplify routine business procedures in a cost-effective and non-invasive manner. On the other hand, big data and analytics play a significant role in decision-making, pattern detection, and a variety of other business-related tasks.

When you pair RPA with advanced data processing and deep learning tools, you will reap even more market benefits. Companies that use RPA and analytics to their advantage would be well ahead of the competition in performance, operating expense, and eventually customer loyalty.

Data Cleansing for Analytics with RPA

This blog post will feature two critical use cases for using RPA in data analytics; Automating data entry, manipulation, and collection with RPA and driving data collection and aggregation with RPA for advanced data analytics and deploying ML models.

Using RPA for Data Entry, Integration, and Migration

Due to a lack of systems integration, business users typically have to manually enter data from documents like invoices into one system and then rekey the same data into another system. In analytics programs, users have also had to manually cull through data to locate address fields, ZIP codes, or names that are either missing or duplicated by other submissions. The process for manual data cleansing and deduplication of records can be strenuous and error-prone.
IT and business users in charge of the data cleansing operations have to deal with tedious administrative labor, which significantly delays business operations. However, if this analysis is not completed, enterprise operations will be disrupted, and the accuracy of analytics results will be jeopardized due to inadequate data consistency.

RPA can be leveraged to create and maintain well-structured and correctly classified data across enterprise systems as well as generate data lakes for developing advanced Machine Learning models for data scientists. The RPA software robots can work through one or more software applications. In this data cleansing and analytics case, RPA can work with Big Data analytics toolkits and help support businesses in the following five ways:

  1. Data entry, rather than manual keying or file sending, can be fully automated.

  2. Automated data migration between disparate enterprise applications, Ie. System migrations during mergers or acquisitions.

  3. Automated data monitoring, RPA can continuously check for data anomalies and improve data consistency without human involvement.
  4. Automated data deduplication and retrieval of new data streams, such as IoT computer logs and other system-generated data.

Although RPA's main objective is to automate transactional data entry and save time for end-users, it can also help with common IT operations, such as the preliminary data cleansing before it is used in analytics.

Using RPA for Data Analytics

Apart from moving data across enterprise systems, RPA is also known to be an excellent tool for data aggregation to better supply data sources for advanced processing algorithms. It is improving data analytics and enabling machine learning that can be used to digitize business operations further. Using advanced data analytics software to analyze this RPA-generated data will help you develop a deeper understanding of the organization's processes and workflows, model process improvements, and pinpoint precise process improvement opportunities.
RPA not only automates but also digitizes business processes. This means that more data can be obtained if the process is automated than when the process was performed manually. This method eliminates subjectivity from manual process assessment and allows organizations to make transformational decisions to meet business goals and augment their competitive edge.
The data produced by RPA can then be subjected to various data analysis types to optimize these processes further. Below are a few of the use cases with RPA-generated data:

  • Machine Learning: We can say which elements influence a process dramatically and to what degree by using machine learning models. You will get prescriptive strategies to optimize the processes if you feed RPA process audit trails into different machine learning algorithms.

  • Process Mining: Process Mining technologies can be used to visualize the whole process leveraging data provided by RPA, allowing for a much more in-depth understanding of the process, and vice versa, Process Mining apps can generate the data to select the right processes for RPA development.

  • Process Simulation: It can be challenging to determine the effects of even minor improvements in complicated and repetitive business processes. However, it is simple to run a simulation program by utilizing process data to define process requirements and simulate the realistic effects of various scenarios in automated systems.

To summarize, adopting RPA has the advantage of adhering to a collection of rules to collect isolated and dysfunctional data. This ensures that the data gathered can be more organized and uniform, making it more beneficial to those who want to access it. At the same time, software robots monitor and report their own behavior. RPA can also be used to analyze and make sense of large volumes of data. This generates a cycle in which RPA and Data analytics help improve each other for the ultimate benefit of the organization.

JOLT Advantage Group is prepared to assist you with RPA implementation and all of your digital transformation requirements.

Contact us to talk to a specialist today about your automation questions and goals, book a free consultation here.