UX Design in Data Projects: Discover the Benefits

Applying UX design techniques to data projects focuses on the user, resulting in more personalized, secure, and intuitive deliveries.

Who hasn’t felt frustrated with client feedback after delivering a data product? 

Or had to redo a lot of work because what you created wasn’t exactly what the client expected?

When we deliver a project that doesn’t meet expectations, we mobilize many resources to restructure it. 

To avoid these expectation mismatches, we can apply UX design strategies throughout the project.

User experience design is a field that focuses on solving customer problems, meeting their needs, and making product use easier and more efficient. 

We can use UX (User Experience) techniques in our data products to make them more accessible, understandable, and tailored to the intended context.

Some of these techniques include conducting interviews and involving users in the product development stages. 

In this article, we will delve into these techniques and the benefits of applying UX design in data projects, helping us deliver what clients need.

Why Use UX Design in Data Projects?

User experience design, or UX design, is a product development methodology where the target audience is central from the project’s inception. 

Using this approach, products are more likely to be user-friendly, recommended by experts, and meet user expectations. 

Additionally, applying UX techniques to data projects can save resources and reduce feedback cycles by aligning the process with client expectations. 

In data projects lacking user focus, the final product often faces difficulties in: 

• data interpretation, 
• deriving insights, 
• or identifying delivery value. 

Many clients may prefer to return to spreadsheets rather than using new data products. 

This gap between what is developed and what is expected can be narrowed by focusing on the user’s specific context, the problem they want solved, and their strengths and needs.

Several UX techniques can help achieve these objectives in a data project.

Data Projects and UX: Best Techniques

1. UX Research

 UX research involves conducting studies to understand the user’s context.

The results ensure a better understanding of the problem the data solution aims to solve. 

Often, products developed without UX consideration are based on distorted user needs. This leads developers to rely on their own experiences and beliefs to create product features.

UX researchers must strive to distance themselves from personal beliefs and adopt a learning mindset with users, without neglecting the functionalities considered.

Personas

Another UX research technique is using personas during development. 

Personas are profiles created to illustrate the standard user of a product. 

They provide essential guidelines throughout the development process to meet the demands and expectations of users. 

In user-focused projects, personas are revisited to remember the pain points, needs, and expectations of users.

In data projects, personas can help understand users’ familiarity with:

• data sets, 
• technology and data visualization preferences, 
• interactivity (like filters and sorting), 
• and knowledge gaps that could hinder product use. 

Identifying these characteristics early in the data project allows for the appropriate approach to creating visualizations and functionalities compatible with users. 

It also helps build better rapport with clients by developing exactly what they need.

2. Interactive Prototyping with the User

Creating mockups and prototypes of our products is another UX practice that benefits data product projects.

Mockups

A mockup is a graphical representation of a product to simulate its final appearance at a low cost. 

Mockups can start with simple lines and low fidelity. They evolve to high fidelity with colors, typography, and identical elements to the final product. 

The creation of various fidelity mockups should follow UX research findings.

Prototyping

Prototyping can be done alongside mockup creation. 

It involves combining screens interactively to simulate the user experience and navigation.

Users and clients review prototypes to validate the information architecture and product navigation.

This allows for continuous testing and feedback before the development stage.

Including users in the creation of mockups and prototypes helps identify usability issues early, saving time and resources. 

Early detection of problems makes modifications easier and less costly. 

Data Products: Mockups and Prototypes

In data products, creating mockups is crucial for ensuring satisfactory and understandable visualization and data storytelling. 

Prototyping allows for quick testing of dynamics like data filtering without actual implementation. 

Mockups and prototypes can be created even without a structured database, providing valuable deliverables to clients while data engineers understand the database. 

This accelerates the project by defining the best visualizations early on.

Tools

Some useful tools for quickly creating mockups and prototypes are Figma and Adobe XD. 

These UX design tools simulate appearance and navigation more simply than data tools. 

They are easy to learn and don’t require prior design knowledge. 

However, mockups can even be created with paper and pen.

Data Accessibility with UX Principles

There are well-studied UX and design principles based on human perception and cognition that can minimize usability errors and make products more intuitive. 

These principles reduce cognitive load, making product interaction easier. 

While these principles should be used cautiously and not overshadow user context information, they provide a good starting point for sketching data products.

Nielsen’s Heuristics and Gestalt Principles

Design principles like Nielsen’s heuristics and Gestalt principles are noteworthy. 

Nielsen’s heuristics offer specific guidelines to identify common interaction issues. 

For example, the consistency and standardization heuristic involves standardizing visual features with the same function, like buttons having the same size and colors throughout the product.

Gestalt principles help understand how users perceive and organize information visually. 

For example, the proximity principle states that people perceive close elements as a group. 

Thus, in data products, elements within a section should be closely spaced, and sections should have more spacing between them to define groups and facilitate perception.

Applying Nielsen’s heuristics and Gestalt principles can create cohesive, intuitive, and user-centered data products, enhancing efficiency and user satisfaction.

Benefits in Data Products

In data products, adhering to these UX design principles simplifies data presentation, avoiding information overload in a single visualization or dashboard. 

These techniques also ensure better information hierarchy and data accessibility. 

Using appropriate color contrast, logical information flow, and considering different skill levels and special needs make the data product suitable for a wider audience. 

This also reduces rework on updating accessibility best practices.

UX Design in Data Projects: Conclusions

Applying UX design techniques to data projects focuses on the user, resulting in more personalized, secure, and intuitive deliveries:

• contextualization and personalization of the data product
• interactive analysis and constant feedback
• intuitive data product design
• saving time and resources
• avoiding rework
• increased client and user satisfaction

This approach ensures a secure estimate of time, resources, and work required, providing confidence that the data product will meet the client's needs.

UX Design in Data Projects by Indicium

At Indicium, our methodologies apply appropriate and customized techniques to build intuitive and tailored products. 

We transform data projects into efficient, secure, and satisfying experiences for you.

Contact us to know about our solutions.

See you soon!

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