Introduction to Presenting Data Analysis Results


Data analysis has little impact if the results are unclear or misinterpreted. Presenting findings effectively requires structure, precision and clarity so that insight is understood rather than assumed. Professional communication of results transforms output into meaning that supports informed judgement and action.

Technique Overview

Introduction to Presenting Data Analysis Results

Introduction to Presenting Data Analysis Results Definition

Presenting data analysis results is the structured communication of analytical findings using visual, verbal and written methods to ensure clarity, accuracy and relevance. It involves selecting formats that support accurate perception (Cleveland and McGill, 1984), preserving data integrity (Tufte, 2001), reducing cognitive overload (Sweller, 1988), and sequencing information to enhance understanding (Mayer, 2020) so that results can inform organisational judgement and action.

Introduction to Presenting Data Analysis Results Description *

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Further Reading

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Introduction to Presenting Data Analysis Results references (4 of up to 20) *

  • Cleveland, W.S. and McGill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79(387), pp.531–554. doi:https://doi.org/10.1080/01621459.1984.10478080
  • Few, S. (2004) Show me the numbers: Designing tables and graphs to enlighten. Oakland, CA: Analytics Press.
  • Hullman, J. and Diakopoulos, N. (2011). Visualization Rhetoric: Framing Effects in Narrative Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), pp.2231–2240. doi: https://doi.org/10.1109/tvcg.2011.255
  • Kosslyn, S.M. (1989). Understanding charts and graphs. Applied Cognitive Psychology, 3(3), pp.185–225. doi:https://doi.org/10.1002/acp.2350030302

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Related Concept: Introduction to Tailoring Communication to Different Audiences

Data only creates value when it is understood. Stakeholders differ in role, influence and technical capability, so communication cannot be uniform (Freeman, 2010; Mitchell, Agle and Wood, 1997). Effective data professionals analyse their audience and adapt language, structure and depth to ensure clarity, relevance and impact.