The real power of Nextanalytics is not in its command set or function list - it is in its' ability to deliver an analytic result very quickly and easily, without a lot of set up or formula construction. In this example, we show how to move from raw survey data to useful information.

We start with the raw survey data, courtesy of stars.ac.uk, looking at the effects of a high or low glycaemic index (GI) breakfast on children's lunchtime calorie consumption. Typical of survey datasets, many of the columns contain code numbers that must be cross-referenced to a lookup table for meaning, and the column labels themselves are frequently almost meaningless representations of what they stand for.

With Nextanalytics, we can start by replacing most of those codes and labels with meaningful text, and then continue our analysis in any number of directions. The code exchange can be a manual exercise, replacing '1' with 'boy' and '2' with 'girl', or done automatically using a lookup table. Each transformation we make is the starting point for the next transformation, allowing you to take your analysis step-by-step and build your to result without a lot of fuss.

With a couple of simple steps, we can normalize the calories consumed into ranges by hundreds, then count the number of children in each range for each measure. This distribution of numbers can be presented ina  number of ways including a simple line chart. As an alternative, Nextanalytics can provide colored formatting on the table of numbers, showing a heat-map-like color scheme with the hot orange highs and the cool blue lows. In this case, it looks like a High GI breakfast tends to raise the calories consumed at lunch.

This ability to get to a result quickly, and to build on that result iteratively, makes Nextanalytics an ideal platform for delivering an insightful analysis with very little effort. This gives you the luxury of time to focus on the results.