# AIGA Design Census 2016: Accessible Data

The second in a series analyzing the 2016 AIGA Design Census.

#### Introduction: Inaccessible Data

Part one of this series concluded the value of AIGA’s Design Census platform. Despite year-one data not offering us the benefit of a time series to validate and ensure accuracy in our findings, it provides important clues to the stories that may emerge when 3–5 years of information has been collected.

But how can we start to find those clues when the raw data looks like this?

There are a total of 219,753 cells of information in the raw US data file. Its presentation is linear and brutally systematic. Each census question is assigned a column. Each participant, a row. The rows are not organized in any specific order. It’s an inaccessible layout made by a computer, for a computer. Solving the design problem of making this data accessible is in many ways about making it human and readable.

#### Accessible Data = Simple Data

In the example shown above, all US data for Census Question 12: How Hired? has been simplified into a single table.

• Answers are now condensed into a single list, removing repetition and reducing the total number of vertical rows from 7697 to 9.
• Numbers are right-aligned, helping the reader identify ‘Job listing’ and ‘Self-employed’ as the most subscribed categories.
• A percentage is also calculated, allowing the reader to quickly translate each answer into a proportion of total US participants.
• This example could be simplified further by organizing categories from highest to lowest.

#### Accessible Data = Contextual Data

In the above figure, Question 12 is shown again in the single table format, but now with a smaller sample set focused on the WEST-Pacific region of the US.

• It’s important to contextualize this regional sample against the whole country. National totals are included in the top row and far right column, helping readers identify that although 25% of answers were for ‘Job Listing’, that number only represents 5.4% of total census participants. Looking to other regions for further validation is required.
• This ‘zoomed-in’ scale also helps readers localize potential anomalies in the data. For example, we’re shown that 4 of the 8 participants who chose not to answer the question are from the WEST-Pacific region.

Zooming-in again to a state level produces this view for California.

• Contextual totals for regional and national levels are included, highlighting the fact that California participants make up 66.6% of the whole region’s data. This considerably weights the WEST-Pacific regional data in California’s favor.

#### Conclusion: Accessible Data = Visual Data

With the aim of empowering designers to overcome the inaccessibility of raw data-sets, this table design has now been applied to 20 of the 50 AIGA Design Census questions, and is available to download on GitHub.

There are still failings to the accessibility of table data. Presented correctly, visual data can communicate faster while at the same time offering richer experiences and deeper narratives. To name just one contributed to the Design Census Gallery, sosolimited have created an interactive tool that lets users plot two census variables against one another to find correlations.

This census is an opportunity to help designers verbalize the questions they may have about their industry. Table data is a valuable stepping stone on the path to visual data, and hopefully this design format helps provide the accessibility needed to make that exploration easier and more manageable.

Here’s how the new layout compares to the raw data shown in Figure 1:

Archie Bagnall is a British Designer and Brand Consultant based in Southern California, and is the current President of AIGA Orange County. Spreadsheets are his jam.

You can visit his website here: http://archiebagnall.co.uk/
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