Regardless of the industry, Robotic Process Automation (RPA) has been transforming businesses by improving efficiency with its immense automation capabilities. We've seen RPA adoption skyrocket in the last few years. RPA automates repetitive business processes to enable the workforce to concentrate on more valuable tasks and innovation. However, to ensure the success of RPA applications, businesses need to provide structured and appropriate data.
How Data Efficiency Impacts RPA Success
Data is the key competitive edge of any business but huge amounts of data are sourced from different enterprise systems in different formats. It is important to have a well-defined procedure to collect, categorize and interpret data to use the data as efficiently as possible for RPA applications. Here we analyze how various data efficiency aspects affects the success of a business' RPA journey.
1. Data Bias
Data bias is the biggest problem that adversely affects the RPA journey and business decisions too. Data bias is predominantly trying to use data set that is not comprehensive enough to analyze. The data could be biased from the source, or become biased after the collection process. Or, the type of data collected could be wrong. Any conclusions made with this data is in danger of being biased. Wrong decisions could be made, resulting in loss of time and resources.
To get better automation results with RPA, clean data is very important in both the development and testing of RPA applications. To avoid data bias ask yourself:
- Is the data set considered inappropriate?
- Is data collection happening with biased thoughts in mind or an incorrect procedure?
- Is data being collected from irrelevant sources?
2. Data Strategy
To automate repetitive tasks, RPA applications are trained with predefined processes and data sets to fine-tune algorithms. Here businesses should follow a robust data input strategy. Based on the business problems RPA is intended to solve, businesses should organize these data sets. No data is bad, but which data formats serve your business needs matters. Similarly, based on the RPA application need, businesses should identify what data sets are required to train the RPA bots and accordingly segregate the input data to the RPA application training models.
3. Data Improvement
RPA applications are not a “launch once, forget forever” type of application. The development team develops the solution to meet application needs. To ensure better efficiency, RPA applications should be sourced with constantly evolved data to ensure continuous evolution of RPA applications and deliver better results. Businesses should have a culture of continuous improvement and upgrades.
Steps to Improve Data efficiency in RPA Implementation
- Checks for data duplication, errors, analysis, and standardization
- Implement data governance programs that define how data is collected and used
- Ensure the data samples represent the real environment
- Don't make random correlations
- Ensure diversity in modeling with more data sources
- Define everything clearly in the data collection process
- Conduct qualitative analysis to detect defects affecting data quality
- Compare results with your real-world scenarios
- Review your results with the RPA strategy team
The success of any automation solution is purely based on the training data feed. If the data is wrong, then the analysis and decisions made out of this data will be wrong. If data is inconsistent, the results will be inconsistent. So, conduct a qualitative analysis on data and processes and continuously test the automation application to ensure successful automation results with RPA implementation.