No abstract

Purpose: Data is collected from all aspects of our lives. Yet, data alone is useless unless converted into information and, ultimately, knowledge. Since data analysts, in most cases, are not the ones in charge of making decisions based on their findings, communicating the results to stakeholders is crucial to passing on information of data-driven insights. That is where the discipline of data storytelling comes into play. Often, data storytelling is considered an effective data visualization. Creating data stories is a structured approach to communicating data insights as an interplay of the three elements data, visuals, and narrative. Sharing data-driven insights to support better business decisions require data storytellers skilled in the “art of storytelling”. Design/Method/Approach: In this paper, the authors discuss the use of data storytelling in business to communicate data to stakeholders for improving decision-making. The findings are derived from (1) an extensive literature review and (2) a qualitative analysis of 13 expert interviews with people incorporating data storytelling into their daily work within their jobs in international companies. Findings: These interviews revealed the importance of providing a flexible tool to support knowledge sharing for people communicating complex data to internal stakeholders. Combining literature with qualitative research enabled the authors to create the "data storytelling cheat sheet", a guide for practical data storytelling. Theoretical Implications: Theories like the Psychological distance or the idea of the theory of dual processing dual are used to base our research idea on. There was no new theory built in this paper. Practical Implications: One of the results is an implementation systematic cheat sheet that helps practitioners to implement data storytelling in their daily business. Originality/Value: The theory of data storytelling is overwhelming the first time to use and based on an empirical study with experts in the field a guideline for hands on use was developed under a based on a cleanly defined empirical study. Research Limitations/Future Research: The paper focus on internal data storytelling – maybe with external stakeholders it might be slightly different. The results the data communication part in any data analytics project. Paper Type: Empirical JEL Classification: D7, D8

No abstract

No abstract

No abstract

No abstract

No abstract

No abstract