Digital transformation and the application of digital technologies present unprecedented opportunities to promote sustainable development and accelerate progress towards achieving the 2030 Agenda. From improving delivery of public services to supporting financial inclusion, from improving agricultural efficiency to promoting decent work and economic growth, the use of digital technologies can contribute significantly to the 2030 Agenda. However, the use of digital technologies is not a silver bullet and it brings its fair share of issues.
The digital divide—the gap between digital ‘haves’ and ‘have-nots’—has been widely discussed over the years, and discourse around the need to bridge digital divide are common. There is a consensus on the need to improve access to digital technologies and promote digital skills to reduce this gap. However, another less discussed concern arising out of the increased digital transformation is the ‘data divide’.
The digital divide—the gap between digital ‘haves’ and ‘have-nots’—has been widely discussed over the years, and discourse around the need to bridge digital divide are common.
This piece discusses the meaning and developmental impact of the data divide, existing efforts on bridging this gap, and the key elements that can help reduce this gap.
Understanding the ‘data divide’
Currently, there is no agreed definition of what ‘data divide’ means, and different interpretations have been given to the concept in existing literature. Lev Manovich established that a “data analysis divide” was growing between data experts and those without proper computer science training. Danah Boyd and Kate Crawford pointed out that a new form of divide was evolving between the ‘Big Data rich’ and the ‘Big Data poor’ based on the accessibility and skills required to analyse and use Big Data. Ralph Schroeder explained that there was a growing divide in the use of Big Data in academic or scientific analysis. Mark Andrejevic highlighted that using data required access and capacity to operate expensive infrastructure and data sets. He expressed that the divide was ‘asymmetric sorting processes and different ways of thinking about how data relate to knowledge and its application’.
Matthew McCarthy notes that existing scholarship highlights two broad elements of the data divide—access to and ownership of big data, and the skills and capacity to use such data. Another facet of the data divide relates to the collection of data and can be defined as the gap between “individuals and communities that have adequate data collected and used about them and those who do not.” Yet another definition limits the gap to those “who have the resources and ability to access and use open government data and those who have not.”
