Big Data in Social Research

Categories: Big Data, Data Analysis, Data Collection, Other, Research, Research Design, Research Skills

May logo- Finding Data in Documents and Datasets

In May we are focusing on Finding Data in Documents and Datasets. You will find the unfolding series through this link. Explore the whole 2021 series on stages of the research process: Finding the Question,  Choosing Methodology and MethodsDesigning an Ethical Study, and  Collecting Data from & with Participants.

Where do we start, in order to make sense of Big Data?

Big data: Data sets so large or complex that traditional data processing tools are inadequate.

In a SAGE Research Methods Foundations article, Buskirk (2020) explains:

Simply put, big data might be considered any source of information that is too large to process using one’s current computing environment. Big data, while certainly large, can also come from a variety of sources and are available at increasing speeds.

He introduces the big data as a process (BDaaP) framework, shown in Figure 1. As researchers we need to think through methodological and ethical choices related to each of these stages: generation, management, and analytics. In short, where did the data originate, how can we manage it, and how can we use it to gain new understandings of the research problem, or prove the hypotheses underpinning the study?

Figure 1. An overview of the big data as a process framework from data generation to management to analytics.

Big Data Generation:
Researchers make distinctions between “made” and “found” data with many traditional social science data collection methods, like surveys, generating “made” data. Big data are often viewed as “found” and the BDaaP framework begins with the generation or sourcing of such information. These data often have major differences in their origins, structure, and attributes compared to the data typically used social science research.

Big Data Management:
The big data management phase of the BDaaP framework involves both processes and supporting technologies for acquiring, storing, preparing, and retrieving the information for analysis (Gandomi & Haider,2015).

Big Data Analytics:
The value proposition of big data often rests on the ability to process and mine data for insights. Analytics refers to stages of the BDaaP framework related to modeling and analyzing data as well as interpretation and insight generation (Gandomi & Haider, 2015).


Buskirk, T. D. (2020). Big Data. In P. Atkinson, S. Delamont, A. Cernat, J.W. Sakshaug, & R.A. Williams (Eds.), SAGE Research Methods Foundations.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

If your academic library has SAGE Research Methods, you have access to the Foundations guidebook.

You can find the whole Foundations library here, and the section on Big Data research here. As with all sources in SRM, you can download it as a PDF, and collect sources into your own public or private reading list. If you do not have access, explore SAGE Research Methods with a free trial. For more, see: A Primer on Getting the Most out of SAGE Research Methods.

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