The ability to “play with the data” is a critical capability in analysis. We utilize this technique in many situations: searching for patterns or trends in our observations; or as another preparatory stage for further analysis. Sorting data in some way – alphabetic, chronological, complexity or numerical – is a form of manipulation.
(This article is the third part in the Deconstructing Analysis Techniques series.)
Manipulating data is that process of re-sorting, rearranging and otherwise moving your research data, without fundamentally changing it. This is used both as a preparatory technique – i.e. as a precursor to some other activity – or as a means of exploring the data as an analytic tool in its own right.
One of the key characteristics of a manipulation technique versus related techniques like transformation is that the underlying data remains unchanged. The main thing we’re doing is changing the relationship – logical or physical – that one piece of data has with another.
Reorganizing the data helps us to identify patterns that may otherwise not be apparent. In fact, it is almost certain that most patterns won’t be visible at first glance.
Let’s start by taking a more detailed look at some of the processes that contribute to the manipulation of data.
Re-sorting is literally a technique aimed at changing the order of the data. Re-sorting is most often carried out on numerical or quantitative data, but can just as easily be applied to text content. There are a few common types of sorting – numerical, alphabetical, chronological; as well as some that are much less common. For example, a list of responses to a survey question asking for a rating of a service might be sorted based on the severity and tone (positive or negative) of the review.
Sorting data helps to isolate significant individual values – the highest or lowest, most-frequent or least-frequent, first or last; and can also be a way of highlighting the shape of the data (more on this later).
Re-arranging is an activity that typically involves the physical or digital repositioning of a data element so that it sits in closer proximity to another. This might be to organize photographs into a narrative; or to juxtapose contrasting ideas for discussion.
Much of the rearranging we do is exploratory, although at times it will be more directed. In these cases we might be trying to present a new configuration for our data – like rearranging furniture – to better support some activity.
Some of this manipulation will be more purposeful. We might be seeking to categorize a collection of photographs by grouping them into similar piles; or draw out common themes in user interviews. Recall, for example, in our article on Deconstruction we talked about breaking out key phrases or ideas into separate data points (on index cards, post-it notes etc).
What are we trying to achieve, though, with all this moving about?
I’ve written previously on the important role pattens play in analysis; and the different types of patterns one might seek to find and identify in research data. The patterns we seek include:
- Trends: the gradual, general progression of data up or down;
- Repetitions: a series of values that repeat themselves;
- Cycles: a regularly recurring series of data;
- Feedback systems: a cycle that gets progressively bigger or smaller because of some influence;
- Clusters: a concentration of data or objects in one small area;
- Pathways: a sequential pattern of data;
- Gaps: an area devoid of observations;
- Exponential growth: rapidly increasing rate of growth;
- Diminishing returns: there is a decreasing rate of growth;
- Long tail: a pattern that rises steeply at the start, falls sharply, and then levels off over a large range of values.
Many design researchers and design practitioners talk about the need to immerse themselves in the data before they can make any kind of sense of it. Manipulating the data is a way of gaining that immersion – that familiarity – through direct engagement.
Designers will undertake this process in a number of ways, depending on the format in which data has been stored. One of the most popular forms of manipulation is to write out key concepts, observations, and ideas onto Post-It notes and stick these to a wall.
The design team them actively moves the physical Post-It notes around, rearranging and grouping concepts and observations to help trigger creative ideas. This technique may be used in both the analysis and design processes to assist the design team, and there isn’t a write or wrong time at which it can be undertaken. This type of exploratory analysis can be powerful, and is a key tool in the card sorting analysis arsenal.
If running the card sort was the fun part, analysis is the painful part, at least until you get going. Exploratory analysis is like playing in the data – looking for connections that make you think “hey, that’s interesting”, or that show patterns of behaviour. – Donna Spencer, Card Sorting
Donna’s quote highlights two important characteristics of this analysis technique: firstly, that it can help uncover and highlight key insights in the design research data; and secondly, that sometimes starting is the hardest part.
Where Do I Begin?
Design research – any research activity, really – can result in a body of data that simply feels overwhelming. Thousands of sticky notes containing observations or notes, covering the walls of a ‘war room’. Perhaps it’s thousands of survey responses, or dozens of interview transcripts. It may be hundreds of photographs taken of users in context; or hours of video of a user testing study.
Sometimes this richness of available data works against us, making it difficult to understand where we should begin. Like it’s counter-part in analysis – Deconstruction – the techniques of Manipulation are easy to undertake, and require little or no preparation.
Perhaps more importantly, Manipulation encourages exploration. It works well as an unstructured activity and therefore works well as an entry point into those vast collections of messy data points we’re so often faced with early in the analysis. If you’re not sure where to begin, begin with manipulation – the more tangible and tactile the better.
Uses of Manipulation
Despite the simplicity of manipulation as a technique, it delivers the heart of some very powerful analytic methods. For example, affinity diagramming is requires little more than manipulation (and perhaps deconstruction as a preparatory technique) to produce some real insights.
In many respects, the method of creating a mental model introduced by Indi Young in her book of the same name is another example of manipulating data with intent. Throughout the method data is manipulated – usually physically – through the use of sticky notes or index cards. Ideas are grouped and compared, collated or isolated, by physically repositioning and rearranging the physical object.
Manipulation can also be used to answer specific research questions. We can sort our data chronologically to find the first occurrence of an event. We can sort the data numerically to identify the highest or lowest values, or to identify the median figure (the middle observation) in a series of observations.
Perhaps we’ve already gone through an exercise of aggregating data points and tallying up the number of occurrences of each. We can now manipulate the data and sort in either ascending or descending order to identify the most common or least common responses. This combination of techniques – aggregation and manipulation – provides for an unsophisticated, but still useful ‘method’.
And that, of course, is one of the key things about each of the analysis techniques discussed in this series: whilst each is useful on its own, their real power comes from the ways in which they are combined to form the sophisticated and rich methods we tend to encounter in books.
One of the greatest challenges we face when we start to play with our research data is a tendency to settle on the first arrangement; the first patterns; the first grouping. We begin with such a chaotic mess, that first glimpse of something that presents us with a clear view – some sense of real meaning – can be quite powerful. We resist the step of re-shuffling and messing it up again, and may therefore miss the opportunity to see a second, third or fourth pattern.
Another major challenge – which we’ve mentioned above – is that the volume of data can be quite daunting. As much as it is a good step to just get started, some sense of how data elements can be grouped or arranged before you begin is important; but it shouldn’t be an obstacle.
And, of course, we must be in a position to easily manipulate the data we’ve collected. This means the format and medium in which our data is recorded is critical. Storing data digitally is not necessarily advantageous or preferable: many designers will attest to the positive effects that can come from physically interacting with the data.
Manipulation can therefore be seen as one of many low level analysis techniques with which we work every day. We’ve all encountered it in one form or another, and probably spent little time considering it. And yet it is one of the major workhorses of any analysis effort, and one which we should understand.