# Data Science Methodologies: Current Challenges and Future Approaches

arXiv: https://arxiv.org/abs/2106.07287 · HTML: https://mlsanity.com/paper/2106.07287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 86 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [E-commerce in Your Inbox: Product Recommendations at Scale](https://mlsanity.com/paper/1606.07154/) (2016)
- [Benchmarking State-of-the-Art Deep Learning Software Tools](https://mlsanity.com/paper/1608.07249/) (2016)
- [Customer Lifetime Value Prediction Using Embeddings](https://mlsanity.com/paper/1703.02596/) (2017)
- [DataHub: Collaborative Data Science & Dataset Version Management at Scale](https://mlsanity.com/paper/1409.0798/) (2014)
- [Data Science Methodology for Cybersecurity Projects](https://mlsanity.com/paper/1803.04219/) (2018)

## Built on by (most cited first, 1 of 1)

- [A survey study of success factors in data science projects](https://mlsanity.com/paper/2201.06310/) (2022)

## Abstract (arXiv)

Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.