Technological advances have made vast quantities of data available for analysis. These data are often generated as a byproduct of technology use rather than a well-controlled scientific process. Many of the traditional tools and algorithms of data science are computationally insufficient for such large quantities of data.
The Center for Scalable Computing and Analysis supports RAND researchers—and their clients—by applying methodological and technical rigor to sometimes confounding questions. Here are some of our key activities, with some examples and links.
At the Center for Scalable Computing and Analysis, we examine data and computing infrastructure, the technology that generates and processes big data, both to their clients and to RAND. We work with other methodologists, such as those focusing on causal inference or mixed methods, to explore how their methods can be applied when the research involves large-scale data sets.
We also develop and use new algorithms to extract useful information from large, policy-relevant data sets, including geospatial images, natural language data in social media, and modeling and simulation outputs. Many of these sources of information would remain inscrutable for analysis without scalable computing and analysis.
Finally, we study the implications of proliferating data and advanced algorithms for society. These include data privacy, equity, ethics, and even geopolitical concerns. We aim to develop tools and expertise that will allow us to better understand and address these challenges.
