What are the most common challenges for Data Visualization storytellers?

1 Data Quality

One of the first and foremost challenges for data visualization storytellers is ensuring the quality and accuracy of the data they use. Data quality issues can arise from various sources, such as incomplete, inconsistent, outdated, or erroneous data, or data that is not properly cleaned, validated, or formatted. Data quality issues can affect the credibility and validity of the data visualization story, as well as the trust and confidence of the audience. Therefore, data visualization storytellers need to be diligent and meticulous in checking, verifying, and documenting the data sources, methods, and assumptions they use, and to be transparent and honest about any limitations or uncertainties in the data.

2 Data Selection

Another challenge for data visualization storytellers is selecting the right data to support their story. Data selection involves deciding what data to include or exclude, what level of detail or aggregation to use, what variables or dimensions to focus on, and what comparisons or relationships to highlight. Data selection requires a clear understanding of the purpose, audience, and context of the data visualization story, as well as a critical and analytical approach to evaluating the relevance, significance, and impact of the data. Data selection also requires a balance between showing enough data to provide evidence and insight, and avoiding too much data that can overwhelm or confuse the audience.

3 Data Representation

A third challenge for data visualization storytellers is choosing the best way to represent the data visually. Data representation involves selecting the appropriate type, format, and style of the data visualization, such as charts, graphs, maps, tables, icons, or images. Data representation also involves applying the principles and best practices of data visualization design, such as color, shape, size, layout, alignment, scale, contrast, and hierarchy. Data representation requires a combination of creativity and logic, as well as a knowledge and skill of the data visualization tools and techniques available. Data representation also requires a consideration of the accessibility, readability, and usability of the data visualization for the audience.

4 Data Interpretation

A fourth challenge for data visualization storytellers is providing a meaningful and accurate interpretation of the data. Data interpretation involves explaining what the data shows, why it matters, and what it implies. Data interpretation also involves adding context, background, and perspective to the data, as well as highlighting the key points, trends, patterns, or outliers in the data. Data interpretation requires a deep and thorough understanding of the data and its implications, as well as a clear and concise way of communicating them. Data interpretation also requires a respect for the data and its limitations, as well as a recognition of the potential biases or assumptions that can influence the interpretation.

6 Data Ethics

A sixth and final challenge for data visualization storytellers is adhering to the ethical standards and responsibilities of data visualization. Data ethics involves respecting the rights, privacy, and dignity of the data subjects, sources, and stakeholders, as well as ensuring the fairness, integrity, and honesty of the data collection, analysis, and presentation. Data ethics also involves acknowledging the power, influence, and impact of data visualization on the audience and the society, as well as avoiding any misuse, manipulation, or misrepresentation of the data. Data ethics requires a commitment and accountability to the ethical principles and values of data visualization, as well as a reflection and dialogue on the ethical dilemmas and issues that may arise.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?