Deconstruction is one of the most frequently used and fundamental analysis techniques in our toolkit. It is used as both a preparatory technique to get research data ready for use in other ways; and a powerful technique in its own right as a method of isolating, exposing, and testing assumptions deeply embedded in our mental models.
One example of deconstruction is turning an interview transcript into a series of separate comments or answers to questions. Deconstruction is often used simply to prepare data for other analytic processes such as manipulation or summarization, or even abstraction.
Note: this article builds on the first part of the series: Deconstructing Analysis Techniques published in February.
Examples of Deconstruction
- Chemical analysis – mass spectrometry: a technique for determining the elemental makeup of a substance or molecule.
- Philosophy/literary criticism: a technique of isolating and testing ideas contained within a work of philosophy or literature
- Systems analysis: identifying root causes through the identification of individual system ‘actors’ and their interactions
- Quality control: unit testing functional components of an application, requires first identifying those components (typically by recourse to the specification)
- User interviews: identifying individual concepts or ideas
- Card-sorting: working with card-pairs
- Task analysis: breaking down complex activities into individual tasks and their components.
There are a wide range of examples of the way in which deconstruction occurs, but our aim is always to reach a definite ‘atomic’ state (where the atom is defined by our research objectives). It should be noted that there are typically more things going on than merely breaking down the data. In the case of chemical analysis, level of elements or compounds are measured; in the case of a stakeholder or user interview, the individual words, phrases or ideas may be tallied, grouped, manipulated or otherwise worked with to form some new insight.
Deconstruction can – and often is – built into the design of the research. We see this in online card-sorting, for example, where data is stored from the outset as card-pairs. Survey results are another example of data where pre-deconstruction is built into the research.
Why, though, this urge to break data down into smaller and smaller pieces?
Smaller, more granular data provides for greater flexibility in the other analysis techniques we need to undertake. By separating ideas or objects out into their own data elements we can have greater control over how elements are treated and positioned with respect to other elements.
For example, splitting a Name element into separate First Name and Surname elements allows us to treat these two components independently, and ask a broader range of questions – such as: “What are the most common first names?”
Smaller, more granular data provides for greater flexibility in the other analysis techniques we need to undertake.
It requires extra effort to break data down and then to store it in more granular form. It also takes effort to request and record extra data during the research process itself. So, whatever level of data granularity we use should be for specific reasons, and to address specific research questions.
Deconstruction represents a powerful analytic technique in its own right. By isolating concepts and ideas, and exposing them to scrutiny on their own, deconstruction highlights the existence of untested assumptions and ‘sacred cows’.
In this sense, deconstruction is often used to analyze problems or situations to which we need to formulate a response. This use of deconstruction allows us to test the reality of perceived constraints: by isolating each constraint to the design, and looking at the conditions under which they may hold true, new possibilities can open up that may otherwise not have been possible or feasible.
Dangers in Deconstruction
There are dangers in deconstruction that are worth mentioning here. At the end of the day our work should lead to something substantively new. This can be difficult if we lose sight of the macro-level problem in pursuit of an understanding of the data in finer and finer detail.
Secondly, in studying the fine detail of our data we can miss seeing the patterns in our data that help drive insights and accelerate the transition to design concepts. At the same time, some patterns only become visible or apparent when we reach a level of granularity appropriate for the data.
Deconstruction can also generate noise in our data which obscures our sense-making abilities. This noise may be the result of data overload – simply having too much information to allow for processing; or it may be that small-scale, natural random variations are masking higher-level trends or patterns. In these cases, the use of summation and aggregation techniques might be an appropriate contrast to the deconstruction technique.
Deconstruction in practice
Deconstruction can often be used in very close association with other analytic techniques. For example, we may break data down into more granular form to facilitate manipulation of that data as a means of inspection or ‘eye-balling’.
In the same vein, we may manipulate or transform our data to allow us to zero in on a particular characteristic – deconstruction in the critique sense of the term.
It may help at this point to look at some examples to help illustrate the different uses of deconstruction as an analytic technique:
A typical interview scenario involves asking participants a series of questions (usually open-ended; sometimes based around topics rather than using a strict question set) and recording the responses. Recording may be through the use of written notes, audio recording, video taping, interviewer/observer notes; and may include a combination of the above.
To begin drawing connections and identifying themes between interviews we need to break down – or deconstruct – the interviews to the level of individual ideas or concepts, feelings, thoughts etc. The medium we use to record each of these ‘objects’ is not important: a spreadsheet might be used just as effectively as Post-It notes or index cards.
Once the data is in this more granular form we can carry out further analysis on the interviews. We may, for example, want to look at the prevalence of positive versus negative feedback.
Note, however, that the need for deconstruction is entirely dependent on the questions we are trying to answer through our research. For example, if our intent was to formulate an impression of the overall level of satisfaction for each interview subject, the deconstruction would be an entirely unnecessary task.
When faced with a failure in a complex system – such as the inability of users to complete a multi-step process, or the appearance of a previously unplanned-for edge case – it is typically quite difficult to diagnose the cause of the failure (in the absence of error handling designed specifically with this in mind). In order to identify the root cause of the failure we undertake a deconstruction exercise to help isolate the components of the systems.
Designing a Car: Highlighting untested assumptions
If we were to begin designing a car we might begin with a brain-storming session and list out all of the components or features that are needed. That list might include items such as “wheels”, “engine”, “fuel”, “doors”, “seats” and a whole range of others. We can now look at each of these features and ask why it’s there, and what it says about our notion of the solution.
For example, ‘fuel’ presupposes a form of combustion engine which, increasingly, may not be relevant. More importantly, ‘fuel’ highlights a range of assumptions – mostly tacit – derived from our mental model of the object ‘car’.
Once these assumptions are exposed we can begin to question their validity in the context of the problem – instead of pre-defining a solution in the statement of the problem. Such questioning, enabled through deconstruction, opens up a broader perspective on the design of a solution.
Deconstruction serves a dual role in our analysis work: as both a preparatory technique to get research data ready for use in other ways; and a method of isolating, exposing, and testing assumptions deeply embedded in our mental models.
The technique is not without its drawbacks: more granular data requires effort to gather and record, store, and analyze. It can also generate ‘noise’ in the data, which can obscure instead of illuminate.
Understanding the role of deconstruction in analysis can help us to better target it’s application to the solution of specific research questions.