Quick Summary

  1. Why is this article interesting?

If you want to translate data into insights you need to consider a few things. This article is intended to show some of these important considerations. Outlining a few of these will give you insights in if you could change your thinking or make you aware of the things you might be doing already.

  1. Why am I writing about this?

Design of information and interaction has always been one of the things that fit my personality very well. Having worked with a lot of developers over time, I also have seen that many people in our field have a deep passion for implementing technical requirements. This post might help the more technical passionate people to realize what else can be important for making apps analytics ready. Making data available is one thing, making people understand and use data in their jobs is another thing.

  1. What will you learn reading this?

This article will cover a few things:

    • What considerations and thoughts on design might be important
    • Get to know what things make it onto your design checklist
    • How to maybe jump start your next project from a design perspective


What is the main reason we would wanna consider anything for our users?

Exactly. Ideally we want our users to ENJOY what they are doing. This in the end is your best ROI.


Many companies that take care of digital products have been forming rules and principles over time to help their developers and designers to conform to a unified way of working. The reason for this being that, this in the end will create a more common and consistent experience for their users.

In the following paragraphs we will dive into some of the more general rules that companies use to get to this point where users of a product get to experience ‘user optimized’ solutions.

What is design?

Oftentimes when you talk about design, people’s first guess is that it is about appearance. To me appearance is just a small fraction of what design means for an analytics solution. I really believe in the way Steve Jobs looked at design:

And to this, I really can’t add anything. Design is just how stuff works. Period.

Alright, lets continue our article now. Lets look into principles and rules that are interesting to data and analytics.

Design Principles

In the next section, I want to describe a few rules that align with these ‘Design Principles’ to which I want to explain when people might be more willing to engage and adopt a solution. Principles for designing an analytics solution have some specific things to consider.

Lets go.


I will start with three principles and for each of those three important considerations (rules) that I find important to think about when designing an analytics solution:

  1. Simplicity;

    is all about simplifying things as much as possible. Doing this helps a user to get and stay on a train of thought. Some considerations for this principle are:

    • Present few(er) options
      • Ask: does this piece of content add to the value of current view? What would be our primary topics looking at the context provided with the data?
    • Be descriptive about your content
      • Can we be more specific with the content provided? Can we add a question to the content to let users know what we are trying to answer?
    • Use common knowledge
      • In every business situation you have people collaborating. Listen carefully to what they say and what they mean. This is where you will find common knowledge. Same is true for creating a user interface. Look at apps and websites, you use those solutions on a daily basis. Ask yourself why some apps work better than others. This is where you will find enjoyable experiences. Try and translate those design patterns into your own solutions.

    You might have heard of this phrase ‘Designers know when they achieve good design, not when there is nothing left to add, but when there is nothing left to take away‘.

    One thing as a practice, to get closer to the above analogy, is to argue with yourself during the design phase of your solution. Doing this and asking, after you draft some things, what the reason is for every element, interaction or piece of content, will get you into learning more about the actual things you are doing. Try it for yourself, it will make you more aware of your own design efforts.

  2. Consistency;

    is all about creating a common arrangement and understanding for the content you provide. It might be useful for this to consider:

    • A top-down approach
      • A top-down approach is a unified way of arranging content, logic and interaction patterns.

This sounds kind of abstract. Let me illustrate with a picture:

The above image shows how multiple concepts related to analytics solutions can be designed with a top-down approach in mind. Although this is pretty common in a lot of digital domains and professions, this example is to make you aware of it. Use this approach in how you apply logic and structure into your thinking and solutions. Doing this over and over again will help to contribute to common knowledge and consistency.

    • Using defaults
      • Default in many cases help people to jump start into a ‘flow’. It can be a simple default as starting the app in the current month of the year. Design defaults can also be a simple color palette that is used consistently in your solution.
    • Repeating patterns
      • Elements, interactions, visualizations, pieces of analysis, layouts and a lot of other stuff will be part of your solution in the end. When users interact or digest any of these things, help them experience ease of use by designing for ‘expected’ behavior. When users click on a button with the description ‘Go to details’ in one sheet, the same button on other sheets of course needs to do the same thing.
  1. Contextualize;

    is all about helping a user to understand data. This is very specific to an analytics solution. Translating data into meaning works best when using the most relevant context for the data and metrics at hand. If you look up definitions on ‘context’ on google, you’ll get this:

Considerations to help people better understand the content you provide can be:

  • Comparing data/metrics against:
    • Dimensional items
    • Prior or historical values
    • Targets or objectives to measure against
    • Overall stats, like averages
  • Choosing conformed dimensions
  • Using a range of relevant analysis types

I know this article contains some abstract concepts, try experimenting with some of the principles and ask others if it works for them. Iterate.

If you have any additional thoughts or comments, please feel free to reach out!

Happy designing and see you next post.