What are the best ways to collect and store process automation data?

1 Define your goals and metrics

Before you start collecting and storing process automation data, you need to define your goals and metrics. What are the objectives and outcomes that you want to achieve with process automation? How will you measure your progress and performance? For example, you might want to track metrics such as time saved, cost reduced, quality improved, or customer satisfaction increased. You should also align your goals and metrics with your business strategy and priorities, and communicate them clearly to your stakeholders and team members.

2 Choose your data sources and tools

When choosing your data sources and tools for measuring process automation results, consider the process automation solution you're using and the data platforms or tools you can integrate with it. For instance, many process automation software have built-in analytics and reporting features that can provide insights on task completion, error rates, or efficiency gains. Additionally, application logs can be used to monitor and troubleshoot processes, and user feedback can help evaluate user experience, satisfaction, and adoption. Log management tools or services can be used to collect and store application logs, while surveys, interviews, or focus groups can be used to collect user feedback which can be stored in a CRM system or spreadsheet.

3 Follow data quality and security best practices

Ensuring data quality and security is an essential part of collecting and storing process automation data. Data quality encompasses the accuracy, completeness, consistency, and relevance of your data. Data security involves protecting your data from unauthorized access, use, or disclosure. To ensure data quality and security, you should validate and clean your data regularly to remove any errors, duplicates, or outliers. Additionally, encrypting and backing up your data can help prevent any data loss or breach that might compromise your confidentiality or integrity. Lastly, you should comply with any relevant data privacy and governance regulations or standards that might apply to your activities.

4 Analyze and visualize your data

Finally, you need to analyze and visualize your data to extract meaningful insights and actionable recommendations from your process automation data. Data analysis is the process of applying statistical or analytical methods to identify patterns, trends, or relationships. Data visualization is the process of presenting your data in graphical or visual formats to make it easier to understand and communicate. To analyze and visualize your data, you should use appropriate techniques and tools, such as descriptive, inferential, or predictive analytics for analysis and dashboards, reports, or infographics for visualization. Additionally, you should share and discuss your outputs with your stakeholders and team members to inform decision making or action planning.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?