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  • Print publication year: 2016
  • Online publication date: June 2018

Introduction to Part 2

from PART 2 - DATA SERVICES AND DATA LITERACY

Summary

If data is ‘the lifeblood of research’, it follows that importance must be placed upon the skills and services required to use this asset in support of the pursuit of new knowledge (Paul Boyle, as cited in Economic and Social Research Council, 2012). At a time when we can barely go a day without encountering another invitation to an event on big data, or come across an article on insight to be drawn from data big or small, there has arguably never been a better time to think about the practice of data analysis and the need for data literacy. In the UK we are fortunate to have investment at the national level in data services, including the flagship UK Data Service, to enable social science and related researchers to discover and use data that has been collected for the purpose of social research. However the practice of research data management (RDM), data literacy and data skills is spread widely across the academy. From the undergraduate student using secondary data in their final year dissertation to the experienced researcher collecting primary data in the field, from the small organization starting to collect data to inform their day to- day decisions to corporates who rely on data for insight into their business strategies, research data management practices can and should be taught as part of a skill set much needed, and increasingly valued, by society.

Data is not new. Good research has always relied on good data as a starting point. What is emerging, however, is a new profession being described, sometimes quite loosely, as data science. Clearly researchers across the disciplines have always managed their data, in ways that have enabled them to contribute to the scholarly debate and literature in their subject areas. What is less evident, except perhaps in some of the hard sciences, is the documentation of the data and methods underpinning this research. How was the data derived, what methods were used, what was included and excluded, what version of data is being used in the study? All these questions, and others, need to be considered before even starting to analyse the data. A good data literacy programme will start with these fundamental questions.

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