The importance of context

 

Context is the condition in which something exists or happens. Context is important in data analytics because it helps you sift through huge amounts of disorganized data and turn it into something meaningful. The fact is, data has little value if it is not paired with context.

Image of a hand putting the final puzzle piece in a 4-piece puzzle

Understanding the context behind the data can help us make it more meaningful at every stage of the data analysis process. For example, you might be able to make a few guesses about what you're looking at in the following table, but you couldn't be certain without more context. 

201028000
200518000
200023000
199510000

On the other hand, if the first column was labeled to represent the years when a survey was conducted, and the second column showed the number of people who responded to that survey, then the table would start to make a lot more sense. Take this a step further, and you might notice that the survey is conducted every 5 years. This added context helps you understand why there are five-year gaps in the table. 

Years (Collected every 5 years)Respondents
201028000
200518000
200023000
199510000

Context can turn raw data into meaningful information. It is very important for data analysts to contextualize their data. This means giving the data perspective by defining it. To do this, you need to identify:

  • Who: The person or organization that created, collected, and/or funded the data collection
  • What: The things in the world that data could have an impact on
  • Where: The origin of the data
  • When: The time when the data was created or collected
  • Why: The motivation behind the creation or collection
  • How: The method used to create or collect it
This is an image of an unlabeled graph with 3 dashed lines (red, blue, and yellow) with a star on the yellow line

Understanding and including the context is important during each step of your analysis process, so it is a good idea to get comfortable with it early in your career. For example, when you collect data, you’ll also want to ask questions about the context to make sure that you understand the business and business process. During organization, the context is important for your naming conventions, how you choose to show relationships between variables, and what you choose to keep or leave out. And finally, when you present, it is important to include contextual information so that your stakeholders understand your analysis.

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