Data analysis, what is this data analysis. Well for one it is a topic that in popping in the IT and AI world. But what is it exactly; data analysis is a way of squeezing out information from data. We live in an age where everything is digital- well almost everything, at least I am not writing this on paper and sending it to people. The foundation of this digital age is data; we are surrounded by data, it might not be present physically but is there. The field of data analytics is on a rise both in terms of technological growth and popularity. But there is a part of field that needs some water on it (not literally like on fire) that is email analytics. Why does an email needs analysing you ask? How many times have you read a mail and come to a different conclusion. There are tools which visualize data in these mails everyday, but even with the intuitive power of visuals, it's easy to draw the wrong conclusions or misinterpret information that's right in front of you.
Just to avoid these drawbacks one can follow these methods:
Bias: We humans are bias about everything, every choice we make, and every step we take. Yeah even about that- we have a favourite parent and with a heavy heart I have to break it to even your parents had a favourite kid (that is if they had more than one). These biases affect the way we look at the world around us. Even with data visualization facilitating a cleaner view into your hard statistics, it's possible for those biases to creep in and affect the conclusions you ultimately take away. One of the strongest examples here is confirmation bias; if you have a preconceived notion about how something works, or a conclusion you've already formed about the way something works, you'll be naturally drawn to data that verifies these conclusions, rather than more powerful data that contradicts it.
Mistake of oversimplifying: It's important to remember that email, like most other functions in a workplace, is a complicated area that can't be reduced to a single numerical inbox statistic. You're dealing with complex human beings, engaging with each other in complex ways, and no one bar graph or pie chart will be able to tell you everything that's going on. The concise demonstrative power of visual data will tempt you into boiling these multifaceted ideas down into bare-bones conclusions, but try not to allow this to happen. Look at data points beyond your basic visuals, and remember the key complicating factors and variables that are influencing this landscape.
The objective: When you open the door to email data, you'll feel like you're walking into a candy store. There are so many options, all of which are interesting in their own ways, and you could easily be drawn in one direction (not the band its own members are leaving it) or another based on how appealing certain data points seem at the time. Knowing what your main objective is according to an organization's goals.
The correct question: Data is objective and the conclusions you are creating from it can be neutral or unbiased illustrations. How your employees actually work, you can have a biased opinion in this case. However, data alone doesn't tell you anything. It's on you to group that data meaningfully, and draw your own conclusions. Because of this, it's on you to ask the right questions of your data. If you're looking for the wrong metrics or interpreting them the wrong way, it won't matter how objective or thorough the data you've collected is.
Focus on actionable takeaways. It's also important to remember that data visualization is not a toy. It's fascinating to peruse different data points, project how your employees are working, and look at interactive graphs that help you form various conclusions about the way your business operates. However, none of this will, by itself, help your organization improve. If anything is to change, you need to focus on forming actionable takeaways from the conclusions you're drawing. Without action and change, your email productivity statistics exist in a vacuum, and can't have any effect on your bottom line.