10.2 The Audience
For many types of routine communication, you will have the ability to select your audience, but in some cases, such as when you are delivering an interim report to your boss or your team, the audience may be pre-determined. Your audience may be composed of other data analysts, the individual(s) who initiated the question, your boss and/or other managers or executive team members, non-data analysts who are content experts, and/or someone representing the general public.
For the first type of routine communication, in which you are primarily seeking factual knowledge or clarification about the dataset or related information, selecting a person (or people) who have the factual knowledge to answer the question and are responsive to queries is most appropriate. For a question about how the data for a variable in the dataset were collected, you might approach a person who collected the data or a person who has worked with the dataset before or was responsible for compiling the data. If the question is about the command to use in a statistical programming language in order to run a certain type of statistical test, this information is often easily found by an internet search. But if this fails, querying a person who uses the particular programming language would be appropriate.
For the second type of routine communication, in which you have some results and you are either unsure whether they are what you’d expect, or they are not what you expected, you’ll likely be most helped if you engage more than one person and they represent a range of perspectives. The most productive and helpful meetings typically include people with data analysis and content area expertise. As a rule of thumb, the more types of stakeholders you communicate with while you are doing your data analysis project, the better your final product will be. For example, if you only communicate with other data analysts, you may overlook some important aspects of your data analysis that would have been discovered had you communicated with your boss, content experts, or other people.
For the third type of routine communication, which typically occurs when you have come to a natural place for pausing your data analysis. Although when and where in your data analysis these pauses occur are dictated by the specific analysis you are doing, one very common place to pause and take stock is after completing at least some exploratory data analysis. It’s important to pause and ask for feedback at this point as this exercise will often identify additional exploratory analyses that are important for informing next steps, such as model building, and therefore prevent you from sinking time and effort into pursuing models that are not relevant, not appropriate, or both. This sort of communication is most effective when it takes the form of a face-to-face meeting, but video conferencing and phone conversations can also be effective. When selecting your audience, think about who among the people available to you give the most helpful feedback and which perspectives will be important for informing the next steps of your analysis. At a minimum, you should have both data analysis and content expertise represented, but in this type of meeting it may also be helpful to hear from people who share, or at least understand, the perspective of the larger target audience for the formal communication of the results of your data analysis.