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Data visualization

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Data visualization isn’t just about charts — it’s about telling a clear and compelling story. This article unpacks a wide spectrum of essential principles for making data easy to understand, honest, and engaging. Ready to transform complex numbers into meaningful insights?

Article by Jim Gulsen
The Ultimate Data Visualization Handbook for Designers
  • The article serves as a comprehensive guide for elevating visualization work, combining technical expertise with design principles to help designers transform raw data into meaningful insights.
  • It provides a point of reference for strategies, methods, and best practices to create more effective and impactful data visualizations.
  • The piece recommends tools and resources that design professionals can immediately implement to enhance the clarity and persuasiveness of their data storytelling.
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23 min read

We data scientists spend so much of our effort helping you understand your users that… you forget that we are users too.

Article by Cassie Kozyrkov
Data Science Effectiveness as a UX Problem
  • The article discusses the need for user experience (UX) design tailored to data scientists, emphasizing the importance of understanding their diverse roles and preferences for creating effective data science tools.
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6 min read

Banking and finance have dwelled in an ivory tower throughout their history.

Article by Adam Fard
Fintech UX Design Trends for 2023
  • Many banks are implementing innovative solutions to make the user experience not only effective but also fun.
  • The article covers the following fintech-driven trends:
    • gamification;
    • product identity;
    • centralization;
    • fully mobile banking;
    • social banking;
    • data visualization;
    • human language.
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5 min read

Visualization of different ways of thinking about and solving complex problems.

Article by Houda Boulahbel
A linear thinker, a design thinker and a systems thinker walk into a bar…
  • The author provides a vivid example to demonstrate the differences between various types of thinking — linear, design, and systems.
    • Linear thinking divides the problem into smaller sections, addressing each one independently.
    • The search for the best solution starts with the user’s needs and behavior in the search for design thinking.
    • With a focus on interactions and relationships between things, systems thinking adopts a more comprehensive perspective.
  • We place a lot of emphasis on linear thinking as a society. The author believes that the key to the most effective solutions lies within all three types combined.
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3 min read
A linear thinker, a design thinker and a systems thinker walk into a bar

The I in AI.

Article by Max Louwerse
How Cognitive Science and Artificial Intelligence Are Intertwined
  • If we want to understand the mechanisms behind AI, cognitive science might come to the rescue.
  • Artificial intelligence and cognitive science have surprising similarities.
  • AI focuses on artificial minds with human minds as an example.
  • Cognitive science focuses on human minds with artificial minds as an example.
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4 min read
How Cognitive Science and Artificial Intelligence Are Intertwined

A deep dive into the map apps rivalry.

Article by Peter Ramsey
Apple Maps vs Google Maps
  • Apple and Google have battled for control of the map applications market for almost ten years. The article provides an illustration of the advantages and disadvantages of each app.
  • The success of Google Maps can be explained by the following aspects:
    • The breadth of data.
    • Better сontextualization of data.
    • Reliability partners for sourcing data.
    • Accuracy of routes and shortcuts for pedestrians.
  • Nevertheless, Apple has some strong features, especially when it comes to handling stressful situations (e.g. calming and very human narration, detailed parking location information).
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7 min read
Apple Maps vs Google Maps

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