Exposing the Hidden Pitfalls of ‘Cockpits’ in Data Visualization

pitfalls user experience

The term cockpit is increasingly used in everyday life. It’s the new buzzword that, seemingly out of nowhere, is trying to sweep aside “Dashboards”, which themselves had already had to deal with “Reporting”. What is a Cockpit ? What’s the difference with a dashboard ? What are the hypothetical impacts of building one ?

First, let’s dive in the etymology behind the word “Cockpit”.

Etymology :

The original meaning was literally “pit where a cockfight happens,” and in the 1700s cockpit became the Royal Navy’s term for the area where a coxswain, or ship’s pilot, was stationed. www.vocabulary.com

Within a data environment

While not officially defined, “cockpit” typically represents a large collection of charts and indicators used for control and decision-making.

In the following five points, I will outline the reasons why I find this term problematic :

1 — Encourages visual overloading

The illustration below demonstrates how a cockpit becomes overwhelmed with numerous charts and KPIs, resulting in stakeholders becoming psychologically detached from a clear understanding of their activities.

The famous “Less is More” is no longer invited to the party.
More charts = More pixels = Less clarity

2 — Encourages excessive data sources and data volume

One of the pitfalls often seen in teams using the wrong terminology (report, dashboard, cockpit, dataviz…) is that they haven’t yet adopted best practices for data modeling and data visualization.

The request : “We need a cockpit that facilitates cross-functional monitoring of the company’s activities, and allows us to get into the details of any services we want”.

3— Overemphasis on control and command

A cockpit with its inherent emphasis on control can pose change management challenges. Moving from reports to dashboards often involves overcoming the obstacle of abandoning the sense of control usually represented by cell form and reports.

It would be easier to make users shift their mindsets from Excel to a Cockpit, compare to a Dashboard. A Cockpit often keeps this same sense of details, high granularity and the use of a multitude of charts. This is less the case for a dashboard where we aim to aggregate data, setup visual exploration and embrace a minimalist approach. The gap here is way more difficult to cross.

4 — Misalignment with user familiarity

The term “cockpit” draws an analogy from aviation, which can be unfamiliar and even intimidating to many business intelligence users. Unlike pilots, who experience extensive training and have specialized knowledge of cockpit controls, business professionals from various backgrounds engage with data visualization interfaces. Using the term “cockpit” may create a disconnect and make users feel overwhelmed, hindering their ability to engage with and make the most of the data available to them.

5 — Time To Market

The sooner your dashboard is available in the company, the sooner you can start generating ROI. Time to market directly impacts the product’s potential goal, allowing you to capitalize on new opportunities and drive business growth. A shorter Time To Market reduces the time between launch and initial feedback, enabling you to refine and evolve your product in the right direction.

Unfortunately, Cockpits are quite long to build because of the complexity related to several points : 
- High number of datasets
- Lack of methodology for this kind of demand
- Lack of UX expertise in the Data Visualization ecosystem 
- Difficult to reconcile diverse requirements and opinions.
- High number of people involved
- Expectations and pressure are higher when it comes to cockpits (often dedicated to a broad population or top management).

Conclusion :

The confusion surrounding the use of the term “Cockpit” in business intelligence highlights the need to re-evaluate our terminological choices. By adopting a more suitable language, such as “dashboard”, we can improve clarity and promote effective data visualization practices.

Let’s reimagine our terminology, if necessary, to create more intuitive data visualization experiences.

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