Big data refers to data sets large enough to require supercomputers. It is less about data that is big than it is about a capacity to search, aggregate and cross-reference large data sets.
Big data rests on the interplay of:
- Technology
Maximizing computation power and algorithmic accuracy to gather, analyse, link and compare data sets - Analysis
Drawing on large data sets to identify patterns in order to make economic, social, technical and legal claims. - Mythology
The widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible.
Social systems are regulated by market (1), law (2), social norms (3) and architecture (4). Big data creates a radical shift in how is thought about research. It changes the definition of knowledge. Big data does not necessarily provide an answer to why questions (e.g. why do people vote for Bush).
Big data claims that it is objective and accurate. However, it is still subjective. The data still requires interpretation which is inherently subjective. Big data also often consists of errors as it mostly consists of data from the internet.
The same methodological issues of smaller data sets exist in big data. Articulated networks refer to networks that result from people specifying their contacts through technical mechanisms (e.g. e-mail; followers). Behavioural networks refer to networks based on communication patterns (e.g. people who text each other). However, these two networks are not equivalent to personal networks.
Context of data is hard to interpret at scale and difficult to maintain when data are reduced to fit in a model.
In order to act ethically, it is important that researchers reflect on the importance of accountability to the field of research and the research subjects. Big data researchers rarely acknowledge that there is a difference between being in public and being public.
Companies that possess the data (i.e. social media companies) often do not equally share their data with all, creating divides in the academia. There are three classes of people in big data:
- People who create data.
- People who have the means to collect the data.
- People who can analyse the data.
There are six important statements regarding Big Data:
- Big data changes the definition of knowledge.
- Big data’s claims of accuracy and objectivity are misleading.
- Bigger data are not always better data.
- Taken out of context, Big data loses its meaning
- Accessible data does not make it ethical.
- Limited access to big data creates new digital divides.
