Author : Basu Chandola

Expert Speak Digital Frontiers
Published on May 17, 2024

The data divide can drastically impact the progress of the 2030 Agenda, thus bridging this divide is imperative to ensure that no one is left behind

Data divide: The new face of digital inequality

Source Image: ©Valery Brozhinsky / Shutterstock

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.”

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.”

In absence of an agreed definition, the scope of the data divide should be kept broad and must cover all kinds of asymmetries around the collection, ownership, skills, capacity and use of data under its ambit.

Data divide is equal to development divide? 

Data is an important tool and a driving force for development as it can improve accountability, transparency, policy formulation, and implementation. Converting of data into intelligence allows the discerning of trends and correlations that can support effective and evidence-based policymaking, and improved public service delivery.

Broadly, data can support the 2030 Agenda in two ways: First, different data sources can complement official statistics to measure, monitor, and evaluate progress on the SDGs and provide accurate evidence for policymaking. For instance, mobile phone spending patterns can be used as a proxy indicator of income levels for SDG 1 (No Poverty). Second, extracting intelligence of public value from data from different sources can be used to further the progress on the SDGs. The use of geospatial data can be used to improve agricultural efficiency for SDG 2 (Zero Hunger).

Converting of data into intelligence allows the discerning of trends and correlations that can support effective and evidence-based policymaking, and improved public service delivery.

The data divide can drastically impact the progress of the 2030 Agenda. The absence of data on a significant section of the society and the lack of access to data or the capacity to analyse the data compounds existing disparities. The emerging data divide leads to a significant development divide and prevents comprehensive analysis, especially in lower-income countries.

Existing efforts on bridging the data divide 

The data divide is increasingly becoming a concern for different forums and agencies. The G20 member countries, under India’s Presidency, noted the growing data divide between and within developed and particularly in developing countries and the need to bridge the data divide. The G20 endorsed the G20 Principles on Harnessing Data for Development (D4D) and welcomed the establishment of the ‘Data for Development Capacity Building Initiative’. This initiative has been developed as the ‘India-UN Capacity Building Initiative’ to develop the capacity of Global South countries and enhance technology and digital infrastructure.

The Zero Draft of the Global Digital Compact acknowledges that data divides can lead to misuse of data and that the benefits of data are currently unequally distributed.

The note by the Intergovernmental Group of Experts on E-commerce and the Digital Economy on “How to make data work for the 2030 Agenda for Sustainable Development” noted that the data divide is compounding the existing divides related to constrained digital access and connectivity between and within countries. Further, the report “Data for development” by the Commission on Science and Technology for Development noted the uneven distribution of the benefits of the data economy and the deepening data divide across the world, effecting particularly the lower-income countries. The Zero Draft of the Global Digital Compact acknowledges that data divides can lead to misuse of data and that the benefits of data are currently unequally distributed.

Bridging the data divide 

To bridge the data divide, it is important to have long-term commitment and collaboration amongst stakeholders across the globe. Though there has been some work in this area, such efforts are rather limited. There is a need for a comprehensive approach to bridging the data divide to ensure that the benefits of data are equitably distributed. Such an approach must focus on developing capacity, providing access to adequate infrastructure and data sets, and developing regulations and policies that support the growth of data-informed approaches to sustainable development.

Promoting investments to support the data ecosystem, capacity building, and infrastructure is another key element of bridging the data divide.

It is important to develop a skilled talent pool with digital knowledge as well as data literacy. Such a talent pool must be diverse and inclusive to ensure different perspectives are embedded in data solutions to reduce bias and discrimination. Promoting investments to support the data ecosystem, capacity building, and infrastructure is another key element of bridging the data divide. Lastly, appropriate data governance frameworks are integral for reducing the data gaps.

With weak and insufficient progress on around 50 percent of the SDG targets, and reversal of progress of around 30 percent of the targets, it is high time that we work towards realising the true potential of data for sustainable development. To ensure that the benefits are not unequally distributed, it is important to consider and focus our attention towards bridging the data divide to ensure that no one is left behind in the data-centric future.


Basu Chandola is an Associate Fellow at the Observer Research Foundation

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