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rom scientific research to small business sales analysis, data analysis provides insight. Data analysis can help companies evaluate how their marketing campaigns are doing and reveal information about customers. Jeffrey Leek from the John Hopkins Bloomberg School of Public Health has studied and categorized six different forms of analyses. Understanding these types can help better organize your data. It also offers some insight into how best to approach based on your goals.


Descriptive analysis is probably the most well known form of data analysis and it’s also the easiest form of analysis to create. Essentially, it takes the raw data and creates a narrative. The general public is used to this form of analysis, as it’s what it used to publish information on census data.


Exploratory analysis is pretty much exactly what it sounds like. It takes a closer look at data to see if there are any connections that can be made but correlation does not always equal causation. Examples of this can be found by looking at completely unrelated data that still shows a downright spooky correlation. Take, for example, this chart comparing the number of drownings with the films of Nicolas Cage.

The number of drownings with the films of Nicolas Cage

(Photo source)


This approach is often the primary aim when it comes to data analysis — to make a determination based on results. Sample sizes are an important part of this type of data analysis. In order to make determinations, it’s vital that the people used be a true representation of the group you want to learn more about.


Looking at trends in sales and inquiries, for example, can help with small business sales analysis and developing marketing plans. This type of analysis can be especially difficult as it isn’t able to take random variables into consideration — it’s just a prediction based on past behaviors.


Causal modes are considered the “gold standard” for data analysis and for good reason. This is the model used in many studies that work to understand how changing one variable will affect another. Examples of this can be found in many medical research papers, such as those that examine the effects of exercise or time spent outdoors with chronic issues such as depression or pain tolerance.


Mechanistic is the most complex form of data analysis. It examines every part of the data, looking at each individual variable in order to understand how the come together. This type of analysis is used primarily in the physical and engineering sciences.