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Time Spent on Data Projects and Where Process Mining Can Help

Time Spent on Data Projects and Where Can Process Mining Help Watches

How do you ensure a successful Robotic Process Automation (RPA) initiative?

It’s a question we receive often. The answer: a thorough understanding of your processes is the key. Clarity helps business leaders see the true face of their challenges.

Torture the data, and it will confess to anything.

Ronald Coase

The quote, although a nefarious adaptation of the original, conveys a cautionary tale to data professionals to decide in advance what analysis to do. Instead of continuously trying different analyses until something works.

Data is meaningless unless worked upon.

However, once understood, you can identify room for improvement, determine the risks involved, and calculate the return and impact of these developments. 

When it comes to RPA, this sort of analysis is key to weeding out harmful bi-products, such as bottleneck shifts, siloed data, automation misplacement, among others.

Learn more about harmful bi-products of RPA gone wrong

ROA Guide: How to Prepare Data to Avodi Pitfalls

Thus, you can achieve high-impact RPA initiatives driven by an insightful Process Mining analysis, and such an effort requires considerable time and energy.

Are you aware of how much time you spend on a proper data analysis?

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Time Spent on Data Science Projects

A typical data science project flows through a few fundamental steps:

  1. Gathering Data
  2. Cleaning Data
  3. Visualizing Data
  4. Model Building/Model Selection
  5. Putting the Model into Production
  6. Finding Insights/Communicating to Stakeholders
  7. Other

In an attempt to map these steps, a 2018 Kaggle Machine Learning and Data Science Survey asked 23,000 data professionals to disclose what proportion of their time is devoted to each phase.

Time Spent on Data Projects and Where Can Process Mining Help Graph

Graph provided by Business Broadway

Data professionals spent nearly 40% of their time gathering and cleaning data.

See the huge portion of time spent on data preparation? 

Some datasets are very messy, sensitive, and scattered. It’s the attention spent enriching data early that improves the accuracy of the outcome.

Once your data is packed and ready to go, Process Mining can cover the remaining 60% of data-related work within your RPA initiative: from end-to-end process visualizations, building models, and communicating results to stakeholders through dashboards. 

Set yourself up for success a RPA initiative because without Process Mining generally leads to failure.

Regardless of the activity, Minit has been helping individuals, teams, and entire organizations with Process Mining so they can spend their precious time on higher value-added tasks.

Here are some examples of such companies. 


Photo by Mitchell Hollander on Unsplash

Robert Heger

28. 11. 2019