Time Management for Data Analysts: Principles and Practice
Time management is the process of organising and allocating time effectively to achieve objectives (Claessens et al., 2007). For data analysts, it is vital to manage multiple projects, datasets, and stakeholders while ensuring accuracy. This technique explores approaches, strengths, and steps to boost productivity and meet deadlines.
Technique Overview
Time Management for Data Analysts: Principles and Practice Definition
Time management is the ability to plan, prioritise, and schedule tasks to make optimal use of available time, enabling timely and high-quality output (Macan, 1994). For data analysts, it involves structuring analytical workflows so that insights are delivered accurately, efficiently, and in line with organisational priorities (Aeon and Aguinis, 2017).
Time Management for Data Analysts: Principles and Practice Description *
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Further Reading
Time Management for Data Analysts: Principles and Practice web and print resources *
Time Management for Data Analysts: Principles and Practice references (4 of up to 20) *
- Aeon, B. and Aguinis, H. (2017) ‘It’s about time: New perspectives and insights on time management’, Academy of Management Perspectives, 31(4), pp. 309–330. doi:10.5465/amp.2016.0166
- Beckmann J, Kellmann M. Self-regulation and recovery: approaching an understanding of the process of recovery from stress. Psychol Rep. doi:10.2466/pr0.95.3f.1135-1153. PMID: 15762394.
- Claessens, B.J.C., van Eerde, W., Rutte, C.G. and Roe, R.A. (2007) ‘A review of the time management literature’, Personnel Review, 36(2), pp. 255–276. doi:10.1108/00483480710726136
- Covey, S.R. (2004) The 7 Habits of Highly Effective People. New York: Free Press.
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