Data are not useful in and of themselves. They only have utility if meaning and value can be extracted from them. In other words, it is what is done with data that is important, not simply that they are generated. The whole of science is based on realising meaning and value from data. Making sense of scaled small data and big data poses new challenges. In the case of scaled small data, the challenge is linking together varied datasets to gain new insights and opening up the data to new analytical approaches being used in big data. With respect to big data, the challenge is coping with its abundance and exhaustivity (including sizeable amounts of data with low utility and value), timeliness and dynamism, messiness and uncertainty, high relationality, semi-structured or unstructured nature, and the fact that much of big data is generated with no specific question in mind or is a by-product of another activity. Indeed, until recently, data analysis techniques have primarily been designed to extract insights from scarce, static, clean and poorly relational datasets, scientifically sampled and adhering to strict assumptions (such as independence, stationarity, and normality), and generated and alanysed with a specific question in mind.
Good discussion of the different approaches allowed/required by small v. big data.