Designing for Data Literacy

Access to data is one thing, being able to successfully work with it is quite another.

A couple of weeks back I had the pleasure of talking to Catherine D’Ignazio about her work with Rahul Bhargava on data literacy. What makes their work so important is how it re-frames working with data as a cultural need and not merely a technical, mathematical or scientific activity.

And they are not alone in this. Back in January 2015, President Barack Obama said:

“… we want every American ultimately to be able to securely access and analyze their own health data, so that they can make the best decisions for themselves and for their families.”

This will take a lot more than simply making data available. It will require a massive and widespread improvement in data literacy. One that necessitates a cultural shift that makes working with data a commonplace activity. Businesses are already acutely aware of these challenges. Often the data literacy levels within an organization are very diverse. But as we know, working with data and using it for decision making is an essential skill for all parts of the business.

Here’s a definition of data literacy:

“the ability to read, work with, analyze and argue with data”.

This is from the paper Designing Tools and Activities for Data Literacy Learners by Catherine D’Ignazio and Rahul Bhargava. Their approach is to look at it not as the ability to use a specific tool, but to promote ways that encourage learning and improve data literacy. They suggest four design principles for supporting learners when building data tools and assets, they are:

Focused - drives to do one thing well

Guided - introduced with strong activities to get the learner started

Inviting - introduced in a way that is inviting to the learner

Expandable - appropriate to the learner's ability, but also offers paths to deeper learning

Data literacy pertains to more than just office analytics: are you up to speed?

If we think about this from a BI perspective it’s plain that one report, one chart or one fixed dashboard for everyone who may need the information simply doesn’t cut it. Often it may require a variety of entry points to the data, ones that support the range in the skills of those who are using them. The trick is to ensure that everyone can read the data clearly, understanding any immediate actions, able to make judgments based on what’s shown and understand what messages and stories are underlying the data representations.

The ultimate goal with data literacy is to support critical thinking about any data or presentation a person receives. This is shifting the individual from consumer of statements to questioner, debater and explorer. For me, this means thinking about data assets in terms of services rather than documents. Using them must be a progressive experience. They may start with simple, basic charts and defined processes but lead through example and engagement to open data discovery and analysis. Here statements are questioned, facts verified, and new hypotheses brought forth.

When designing for data literacy, think about the principles that D’Ignazio and Bhargava put forward. Set a context and focused use for the assets you create. Don’t build sprawling, complex apps that switch states or work with multiple concepts across unrelated problems. Ensure that any steps needed in the analysis process are clearly signposted. Be wary of forcing people through a flow. Where possible allow for them to find their own path, even if that means bypassing your intentions. Keep the content meaningful and make sure that it’s inviting and not overwhelming for initial use. Don’t simply dumb it down, find ways to reveal the important methods and ideas in the analysis as well as surfacing what’s happening. Consider using approaches like natural language explanations of chart data to communicate information about a chart alongside it, thus helping learners spot the sort of information they should be looking for.

Most of all remember to leave things open. Find ways to encourage people to explore and question the data further. Give them a means to create fresh interpretations and help them voice their findings. Because ‘arguing with data’ is the key to better decision making. To be truly data literate we need to be able to use it both in support of our own ideas and to reveal the flaws in them. That’s how we get to better decisions, made by more people, more of the time.

For further reading on data literacy, here are a few links:

Designing Tools and Activities for Data Literacy Learners – Rahul Bhargava & Catherine D’Ignazio - Catherine D’Ignazio

What Would Feminist Data Visualization Look Like - Catherine D’Ignazio

Talking Visualization Literacy at RDFviz - Rahul Bhargava


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