Why Data Science Projects Fail

TL;DR

This paper discusses the key factors influencing the success of data science projects, emphasizing data availability, algorithms, and infrastructure, and explores reasons behind project failures.

Contribution

It identifies critical components affecting data science project outcomes and analyzes common pitfalls leading to project failures.

Findings

Data availability is crucial for project success.

Inadequate infrastructure hampers effective data processing.

Algorithm choice impacts project effectiveness.

Abstract

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure