Critique by Design with Tableau (MakeoverMonday)

Orignal Data Vizualization

Infographic: Where Do People Retire The Earliest (And Latest)? | Statista

This visualization is an infographic titled “Where Do People Retire The Earliest (And Latest)?” It shows the average effective labor market exit age in selected European countries in the year 2020. This is likely to be a measure of the average age at which people actually leave the labor force, not necessarily the official retirement age. Countries are colored according to the average exit age, with darker shades representing older exit ages. The source of the data is indicated as the OECD.

I’ve chosen to create a data visualization focused on Retirement Ages Around the World. This visualization is based on a dataset titled ‘Pensions at a Glance: OECD and G20 Indicators,’ which was published by the OECD. It specifically highlights the ‘Effective Labour Market Exit Age’ from various countries around the world. My redesign will emphasize clarity, accessibility, and aesthetic appeal, making the data more approachable and understandable for a broader audience.

About the dataset:

The OECD Pensions at a Glance Database has been developed in order to serve a growing need for pensions indicators. It includes reliable and internationally comparable statistics on public and mandatory and voluntary pensions. It covers OECD countries and aims to cover all G20 countries. Pensions at a Glance reviews and analyses the pension measures enacted or legislated in OECD countries for workers entering the labour market at age 22 in the specified year. It provides an in-depth review of the first layer of protection of the elderly, first-tier pensions across countries and provideds a comprehensive selection of pension policy indicators for all OECD and G20 countries.

Critique using Stephen Few’s Data Visualization Effectiveness Profile

Reflecting on the data visualization critique using Stephen Few’s Data Visualization Effectiveness Profile, here are my insights:

The map effectively highlights retirement age differences across Europe with intuitive color coding, though this could be improved for clarity. Using uppercase letters in the title might affect readability; sentence or title case could be more accessible. The font size hierarchy is clear but the footnote needs a larger typeface for better legibility. The font color and background contrast well, aiding readability. The neutral background rightly emphasizes the map’s colors. However, a clearer explanation of the EU-27 average would be beneficial. The map targets those interested in socio-economic trends, policymakers, and labor market professionals, providing a quick comparative view. Adding detailed explanations would further enhance its utility for those seeking deeper data understanding.

This visualization evaluation method offers a detailed critique but is less focused on the constraints and context of the visualization’s creation compared to the Good Charts method, which emphasizes storytelling through data. To enhance the visualization:

1. Add a Gender Comparison: Introducing gender analysis can uncover disparities and offer a fuller picture of retirement ages.
2. Improve Color Differentiation: Adjusting the color palette can better distinguish between data points, especially with the addition of gender comparisons.

Primitive Wireframing

I decided to redesign the data visualization to encompass a global perspective, moving beyond the initial European focus. This broader approach aims to engage an audience interested in worldwide trends. My redesigned wireframe features a world map with diverse data points, catering to international viewers like policymakers, economists, social scientists, and educators. My goal is to provide a macroscopic view that fosters in-depth discussions and comparisons about retirement, helping inform decisions at both individual and policy levels.

Critique of the primitive wireframe:

I utilized the following questionnaire to gather feedback on my wireframe. I wanted to capture people’s immediate reactions and understandings without preconceived notions shaped by my explanation.

Orientation: I started by presenting the visualization without much preamble, curious to see how individuals would interpret the information without my input.

Seed Questions:

  1. I asked participants to express their initial thoughts: “Can you tell me what you think this is?”
  2. Then, I probed further: “Can you describe to me what this is telling you?”
  3. I sought to identify any points of confusion or surprise: “Is there anything you find surprising or confusing?”
  4. To understand the effectiveness of the design, I inquired: “Who do you think is the intended audience for this?”
  5. I was also interested in their personal input: “Is there anything you would change or do differently?”

Follow-up Questions (based on their responses):

  1. I wanted to know what stood out: “Was there anything that particularly caught your eye?”
  2. Clarity was key, so I asked: “How easy was it to understand the information presented?”
  3. Aesthetics are important in data visualization, so I queried: “Did you find the colors and layout to be visually appealing?”

Conclusion: I wrapped up the feedback session with an open-ended question: “Do you have any additional thoughts or suggestions?”

This approach allowed me to gather rich, qualitative data on the visualization’s effectiveness.

Interviewee A (Student, mid 20’s)

Interviewee A scrutinized my initial color choices and implications for data interpretation. He expressed difficulty in discerning patterns and suggested a color palette that varied in hue and also brightness. Additionally, he recommended features such as hover-over details and clickable regions for dynamic engagement with the data.

Interviewee B (Student, mid 20’s)

Interviewee B emphasized the storytelling aspect of data visualization. She pointed out that the gender trends over time were particularly compelling but were lost amongst all data. She proposed larger, more interactive trend displays that could capture the historical journey of retirement ages.

Reflecting on the interviews with two students, I found commonalities and differences in their feedback. Both highlighted the need for simplicity and interactivity, indicating that my initial design was overly complex and static. While one student focused on color scheme and technical clarity, the other was interested in the narrative potential, emphasizing the importance of trend lines. This feedback underscored the importance of balancing aesthetic appeal with functionality in data visualization, highlighting the need for accessibility and engaging storytelling. From this, I learned the significance of a user-centered approach, realizing that it’s crucial not just to present data, but to weave it into a clear, interactive story that addresses the audience’s varied needs.

Here’s how the feedback influenced my final design:

Final Visualization

In my redesigned data visualization, I present a global view of average retirement ages in 2020, with a focus on gender disparities. Opting for a global perspective, I aim to highlight both the diversity and commonalities in retirement trends worldwide. The visualization features an interactive world map, using color gradients to indicate different retirement ages and provide visual cues about regional trends. Additionally, I included line graphs to show the historical progression of retirement ages for men and women, emphasizing gender dynamics. My goal was to transform data into a tool for discovery and understanding. Interactive elements encourage users to explore each country’s specific situation, while comparative lists of ‘Late Bloomers’ and ‘Early Exit’ countries offer quick insights into notable exceptions.