A number of useful ways exist for processing and using language data, among them pre-processing raw language data for specific uses, focusing on an efficient data sample and distilling the data for particular purposes using tools like net-touch, affinity diagrams or KJ analysis.
One problem inherent in language data is the volume of raw material necessary to mine the most useful information. When capturing voice of the customer (VOC) data it is helpful to first pre-process information to highlight context and needs data, as discussed in Part 1. Figure 1 illustrates this for a simple case. Extracting key phrases, while maintaining a clear and traceable link to their sources, is the first step in preparing the data for further distillation.
|Figure 1: Pre-Processing Transcripts to Highlight Context and Needs Data|
Even after pre-processing, there still may be too much data to readily distill. In these cases, further pruning of the data to an appropriate representative sample can save time and energy. For example, the KJ analysis method uses a high level of discipline that requires considerable time be spent on each language data element.
What if pre-processing has yielded 100 or more remaining language data groups? A simple approach that works very well in practice is illustrated in Figure 2. The steps to this multi-pick approach include the following:
Net-touch, a tool for distilling the data, uses a simple affinity process with a useful twist. In a routine affinity, everyone’s self-stick notes are posted on a wall and each is read by enough members of the team to begin the grouping process. In net-touch, everyone holds onto their own notes and watches the facilitator for cues to offer a note for grouping. A practical use for this process is during the building of an interview discussion guide. The process includes the following steps:
KJ analysis is a powerful tool that is not widely used. Its founder, Jiro Kawakita, realized the simple yet profound value in the way that abstraction distills meaning, even in language and observational data that is incomplete. There are many kinds of KJs each distinguished by the theme question posed in its upper left corner (see Figure 3 and the example in Figure 4). It’s important to note that all KJs seek facts that answer the theme question. For that reason, you won’t typically see this method used for brainstorming. Part of the KJ discipline is the use of report language and the verification source data.
The best way to learn the KJ analysis method is to participate under the guidance of someone who knows and understands the practice. The steps outlined here provide some insight into the process.
The importance of understanding VOC through effective application of language data is clear. Six Sigma practitioners who use this data reap the rewards of combining the benefits of language data with number data to find project success.