Recently, expectations for data science are increasing tremendously regardless of specific fields including the private sector and the public sector. In particular, companies want to have data competitiveness among various competitive factors. As a result, they are attempting to apply data science to all business areas, including product development, marketing, research, services and operations. In many initiative level analytics and data science projects show high success rate. However, in application to integrated solutions that can gain substantial business value, the success rates are very low.
“More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning. Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.”
Why So Many Data Science Projects Fail to Deliver: Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles. – Mayur P. Joshi, Ning Su, Robert D. Austin, and Anand K. Sundaram, March 02, 2021, MIT Sloan Management Review Magazine Spring 2021 Issue
The fact is that successful delivery of data science projects can be challenging, as many studies, including Gartner Group research (87% of data science projects fail) indicate. Jim Weldy, ML/AI consultant at Singularity Systems, wrote in his article “Why data science projects fail” that the root cause is the disconnect between the business user and the data scientist. (https://singularitysystems.com/why-data-science-projects-fail/)
This project is about how to apply data science in the real world to solve problems and create integrated solutions as well as how to translate real-world problems into data science problems and how to implement real world data science project. As expected outcomes of this project, I hope to see well organized data science initiative guidelines and data science engineering process by examining how data science projects in the real world are initiated, designed, implemented, deployed to work in the line of business and engineering actual project.
