Big-Data/Analytics Projects Failure: A Literature Review

Gianna Reggio · Egidio Astesiano

26 citations0/0 files storedEuromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Big Data and Business Intelligence Software Engineering Techniques and Practices Big Data Technologies and Applications

Abstract

Nowadays, no one doubt that big data and analytics are fundamental tools for successfully running an enterprise. However, big data/analytics projects do not seem to be all sunshine and rainbows, and this is documented by many sources providing startling figures of failed projects. Motivated by such figures, we conducted a (grey and scientific) literature review aimed to answer: RQ1) Which are the documented cases of failed BD/A projects, and which are the root causes of their failures?RQ2) What is assumed useful to reduce the chance of failure of BD/A projects? We collected and examined 188 sources. The survey resulted first in a list of hints for helping avoid the failure of the BD/A projects (RQ2), and in a list of 21 cases of failed BD/A projects documented in the literature (RQ1). The analysis of those failures resulted in confirming the hints of RQ2 and prompting other relevant ones. The result of this study will be useful not only to people developing a BDA project but also to researchers investigating the challenges proposed by a BDA project to the traditional methods of software engineering.

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References

2016Big data team process methodologies: A literature review and the identification of key factors for a project's success 2019Big Data Impacts and Challenges: A Review 2017Achieving Competitive Advantage Through Big Data: A Literature Review 2018The Challenges of Business Analytics: Successes and Failures

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2023A new perspective of BDA and information quality from final users of information: A multiple study approach 2024Enhancing big data analytics deployment: uncovering stakeholder dynamics and balancing salience in project roles

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