What skills do you need to analyze process automation data?

1 What is process automation data?

Process automation data is the information generated by your process automation tools and systems, such as robotic process automation (RPA), business process automation (BPA), or intelligent automation (IA). This data can include metrics such as execution time, success rate, error rate, throughput, savings, and return on investment. It can also include qualitative data such as customer feedback, employee satisfaction, and compliance. Process automation data can help you monitor, evaluate, and improve your process automation initiatives.

2 Why is process automation data analysis important?

Process automation data analysis is the process of collecting, organizing, interpreting, and presenting process automation data, which is essential for assessing the effectiveness and efficiency of your solutions, identifying bottlenecks and gaps, optimizing workflows and configurations, demonstrating the value of investments, and reporting outcomes. It is also a great way to communicate your achievements.

3 What skills do you need to analyze process automation data?

To analyze process automation data, you need a combination of technical, analytical, and business skills. These include data literacy, the ability to understand, manipulate, and use data in various formats and sources; data visualization, the capacity to create and present clear, concise, and compelling charts, graphs, dashboards, and reports; data analysis, the capability to apply statistical methods, tools, and techniques to analyze and interpret data; data storytelling, the skill to communicate your data findings and insights in a meaningful and engaging way; process automation knowledge, the proficiency to understand the principles, concepts, and best practices of process automation; as well as business acumen, the understanding of the goals, needs, and challenges of your organization and stakeholders.

4 How can you develop your process automation data analysis skills?

To develop your process automation data analysis skills, consider taking advantage of online courses, books, blogs, podcasts, and webinars that cover topics such as data analysis, data visualization, data storytelling, and process automation. Additionally, practice with real-world data sets and scenarios that relate to your industry, domain, or function. Utilize tools and platforms that simplify and automate data collection, preparation, analysis, and visualization such as Excel, Power BI, Tableau or Qlik. Moreover, seek feedback and guidance from experts, mentors, peers or communities that have experience and expertise in process automation data analysis. Finally, apply your skills to your own process automation projects and challenges and share your results and learnings with others.

5 What are some examples of process automation data analysis?

To give you some inspiration, here are some examples of process automation data analysis in action. A financial services company used RPA to automate the processing of loan applications and reduced the processing time from 2 days to 2 hours. They implemented data analysis to measure customer satisfaction, employee productivity, and operational costs. In the healthcare sector, an organization used BPA to automate the scheduling of appointments and reminders for patients and staff. Data analysis was used to evaluate the effectiveness of BPA on patient retention, staff efficiency, and quality of care. Moreover, a manufacturing company employed IA to automate the quality inspection of products and defects. Data analysis was used to optimize the IA algorithm and configuration, as well as to identify the root causes and patterns of defects. Process automation data analysis is a valuable skill that can help you leverage the power and potential of process automation. By mastering this skill, you can enhance your process automation performance, impact, and value.

6 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?