Principles of the Data Analysis Lifecycle
Understanding and applying the data analysis lifecycle is critical for data analysts. This methodology guides the systematic transformation of data into actionable insights through a structured set of phases. It ensures rigour, consistency, and value delivery in real-world data projects.
Technique Overview
Principles of the Data Analysis Lifecycle Definition
The data analysis lifecycle refers to a series of defined stages that guide analysts through the process of defining a problem, acquiring and preparing data, conducting analysis, evaluating results, and deploying insights to stakeholders. Lifecycle methodologies such as CRISP-DM and KDD promote standardisation, quality assurance, and reproducibility in analytics projects (Saltz & Stanton, 2017).
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Further Reading
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Principles of the Data Analysis Lifecycle references (4 of up to 20) *
- Bannon, W. M. (2013) The 7 Steps of Data Analysis: A Manual for Conducting a Quantitative Research Study. StatsWhisperer Press.
- Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996) ‘From Data Mining to Knowledge Discovery in Databases’, AI Magazine, 17(3), pp. 37–54. https://doi.org/10.1609/aimag.v17i3.1230
- Faleye Quadry Folorunsho, Ifeanyi Kingsley Egbuna, Nwachukwu, O.O., Goodness Damilare Atolagbe and Adegbola, M.O. (2025). Leveraging Data Analytics in Manufacturing Sector to Enhance Sustainable Operational Process and Waste Management. International Journal of Future Engineering Innovations
- Mariscal, G., Marban, O. and Fernandez, C. (2010) ‘A Survey of Data Mining and Knowledge Discovery Process Models and Methodologies’, The Knowledge Engineering Review, 25(2), pp. 137–166. https://doi.org/10.1017/S0269888910000032
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