Data divide

General advances in technology, computing power, storage, and information management practices have enabled huge quantities of data to be both produced and analyzed.[6]

Tim Berners-Lee notes an increasing detachment between people and their own personal data, that regular users of digital devices or other services do not have the same capabilities to utilize data to the extent that ICTs or data brokerage firms do.[7] Berners-Lee argues that if personal data has the potential to benefit users, then users should be able to access and utilize them. However, Mark Andrejevic notes that even if users were given access to their own data, they would not be able to put it to use as effectively as data collectors because user data is not collected in isolation.[7] Instead, data collectors accumulate data within a broader environment. By connecting and individual's interests to the profiles of other users, collectors can filter through content and interest patterns to recommend the content they may deem relevant to an individual. The capacities for storing, collecting, and analyzing data requires the necessary technological infrastructures, datasets, software, and processing power.[7] Being able to extract information out of large datasets necessitates access to machines, databases, and advanced algorithms.[7]

Smart city infrastructures are emblematic of the ways that sensors facilitated by big data technologies capture information to manage issues in urban city centers. Processing technologies may relate to issues around finance, trade, social welfare, etc.[6] For example, municipal governments may identify and monitor citizens, companies, organizations, update their records, map profiles, perform data analyses to spot trends or issues, track services, and so on.[6] Many smart city systems track data on a granular level, and while some governments have opted for open data approaches with dashboards and KPIs on display, some governments are not fully transparent and do not share their processes with the public.[6] In this sense, the data divide is represented by governments capturing real-time data on citizens, whose data is used to further manage and govern city centers.[6]

Being able to work with large-scale datasets and having the skills to navigate them requires types of knowledge that tend to only be available to those who have access to advanced machines, databases, and algorithms.[7] Large databases enabled by big data are too large for any person or any group of people to understand on their own, which is why companies use tools and technologies assisted by artificial intelligence, algorithms, and so on.[6] This divide is not only in the sense of those who have and those who do not.[7] This divide also exists in categorical processes, ontological ways of thinking of data, and application of data.[7] Companies with access to data are able to engage with robust and complex means of sorting. Individual use of collected data simply cannot measure up, especially if enabled within new work environments or communities without proper knowledge sharing or training.[7] For example, failures to adopt new technologies into key industries such as agriculture represents aspects in both the digital and data divide.[8] Lack of data literacy can lead to data deluges – the burden of having and overwhelming amount of data without the capability to extract any meaningful information from datasets.[9]

A lack of collected information can create disparities which can eventually lead to information poverty.[9] Information poverty stems from a lack of data about a given concept, where the data poverty can have a cumulative effect. This can snowball from individuals to governments on a national scale.[9] This may become especially problematic when considering that those with access to information can act on data and thereby influence the lives of others in ways that people may not be able to concretely see.[7] For example, a 2007 report from the World Health Organization shows that health information is one of the six fundamental building blocks of a well-functioning health system.[10] Access to quality health data is essential to resolving outbreaks, sicknesses, or other disparities in health; however, many countries, particularly in the Global South, do not have access to the relevant data sources that would allow them to otherwise address health inequities.[9]