by Evelyn Münster
IoT systems are complex data products: they consist of digital and physical components, networks, communications, processes, data, and artificial intelligence (AI). User interfaces (UIs) are meant to make this level of complexity understandable for the user. However, building a data product that can explain data and models to users in a way that they can understand is an unexpectedly difficult challenge. That is because data products are not your run-of-the-mill software product.
In fact, 85% of all big data and AI projects fail. Why? I can say from experience that it is not the technology but rather the design that is to blame.
So how do you create a valuable data product? The answer lies in a new type of user experience (UX) design. With data products, UX designers are confronted with several additional layers that are not usually found in conventional software products: it’s a relatively complex system, unfamiliar to most users, and comprises data and data visualization as well as AI in some cases. Last but not least, it presents an entirely different set of user problems and tasks than customary software products.
Let’s take things one step at a time. My many years in data product design have taught me that it is possible to create great data products, as long as you keep a few things in mind before you begin.
As a prelude to the UX design process, make sure you and your team answer the following nine questions:
1. Which problem does my product solve for the user?
The user must be able to understand the purpose of your data product in a matter of minutes. The assignment to the five categories of the specific tasks of data products can be helpful: actionable insights, performance feedback loop, root cause analysis, knowledge creation, and trust building.
2. What does the system look like?
Do not expect users to already know how to interpret the data properly. They need to be able to construct a fairly accurate mental model of the system behind the data.
3. What is the level of data quality?
The UI must reflect the quality of the data. A good UI leads the user to trust the product.
4. What is the user’s proficiency level in graphicacy and numeracy?
Conduct user testing to make sure that your audience will be able to read and interpret the data and visuals correctly.
5. What level of detail do I need?
Aggregated data is often too abstract to explain, or to build user trust. A good way to counter this challenge is to use details that explain things. Then again, too much detail can also be overwhelming.
6. Are we dealing with probabilities?
Probabilities are tricky and require explanations. The common practice of cutting out all uncertainties makes the UI deceptively simple – and dangerous.
7. Do we have a data visualization expert on the design team?
UX design applied to data visualization requires a special skillset that covers the entire process, from data analysis to data storytelling. It is always a good idea to have an expert on the team or, alternatively, have someone to reach out to when required.
8. How do we get user feedback?
As soon as the first prototype is ready, you should collect feedback through user testing. The prototype should present content in the most realistic and consistent way possible, especially when it comes to data and figures.
9. Can the user interface boost our marketing and sales?
If the user interface clearly communicates what the data product does and what the process is like, then it could take on a new function: sell your products.
To sum up: we must acknowledge that data products are an unexplored territory. They are not just another software product or dashboard, which is why, in order to create a valuable data product, we will need a specific strategy, new workflows, and a particular set of skills: Data UX Design.
Originally posted HERE
