The Three Step Plan for Data Literacy

In Kenya right now everyone is talking about Open Data. The Open Data Kenya Initiative is over a year old. It represents an attitude towards public data and information that is powerful because of its underlying global ideals of transparency, accountability, community and participation. Open data, when it works is a step towards an honest and more inclusive society.

Today, in places like Kenya, where money and energy are being poured into these data driven projects, there are two first steps that are being taken:

1) Get the data open and online. From open source data collection projects to government level initiatives to liberate data, there is a growing collection of open data.

2) Get people online. Mobile phones, especially, are driving this effort.

These two steps are not enough to create data access.

Technical access to data is not automatically access. For this data to be meaningful the content must be accessible conceptually. Someone who has never used or understood a regression isn’t going to benefit just simply because it is online or she can query the dataset via sms. People need to know what the data looks like and means. We need to foster data literacy.

Data literacy takes two forms: education and representation. Education is more obvious. We need to continue and expand efforts to teach people to be data literate–familiarizing people with common charts, maps, and tables so they can access the data in them, and importantly be more critical of the data behind them.

Data literacy through representation means taking a dataset and representing it in a way that is easy to understand. Commonly in the US we take geographic data and put it on maps, like google maps. Or we take a table and plot it as a line graph. Or increasingly artists create powerful infographics for major newspapers or blogs. These artists and data experts are the translators of data who make more people data literate by writing the data in a different, more familiar, language. No economics PhD or accounting degree requires.

The current approach for expanded data access in Kenya is to encourage these translators. Individuals who can take data and represent it in a way that a wider audience can benefit from it. These translators are often young techies around the world–creating apps and infographics. Apps which can analyze a dataset into a singular recommendation or prediction–infographics that show you how many phones are in Kenya and can thus inform business or NGO strategies.

But we still need more and different translators. The language of hackathons and apps is still out of reach for most people in slums and developing communities around the world. We need cheap oakleys to foster local data literacy through expanded education and local translators. They need to create new local forms of representation. Maps that don’t always rely on the global coordinate system but instead use local landmarks. Charts that don’t just use vertical lines or millions of dollars to compare two costs but use local stories, imagery, and experiences.

The third step will be the key step for open data–as it will make the first two count. In August, I spent three weeks researching data literacy in slums around Nairobi to find ways of making step three more local. Over the coming months we will be sharing some of the preliminary examples and inspirations from this research.

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