New technologies have completely altered the ways that citizens can access data. Indeed, emerging online data sources give citizens access to an enormous amount of numerical information that provides new sorts of evidence used to influence public opinion. In this new environment, two trends have had a significant impact on our increasingly data-driven society: 1) the increasing use of large-scale databases within the open data movement, and 2) the growing use of big data. The open data movement supports the availability of high quality data sets collected by national statistics offices and non-government organisations for a specific purpose. The open data movement has had significant success in recent years in persuading major data providers, and national statistics offices, (for example, the Australian Bureau of Statistics [ABS]) to give citizens access to huge databases in order to create new variables, and explore new relationships. This new access to data is having a profound impact on teaching statistics and modernising curricula to prepare students for a world filled with open and big data, or the so-called "data deluge". However, competent use of large-scale data predominantly requires comprehension of data tables, which are routinely used in daily life and in the workplace to communicate information about large data sets. This article discusses how to implement a framework for helping students develop better ability to interpret large-scale data tables, in particular by using strategies that make comparisons between and within the categories of data and by drawing inferences about data within its context while making any reference to the contextual factors.