8 UX Elements of Best-in-Class Analytics Platforms

Why Does User Experience Matter with Analytics Platforms?

Not only does an excellent UX improve the adoption of the analytics tool within your organization—and therefore, increase the data-driven decisions being made—but it also makes it easier for users to find the insights they need.

A great UX should also serve the needs of many users. For example, both technically-savvy data scientists and upskilling analysts need to be able to investigate the data in the platform without spending too much time guessing which parts of the tool to use next. On the other side of that process are the executives and other business stakeholders who will use the same analytics tool to look at reports and decipher how to use the findings to build their strategies.

In each case, it’s vital to provide these different users with a simple and consistent user experience, one that guides them through the data analytics platform in a seamless way that combines data and insight with transparency.

8 UX Elements of Best-in-Class Data Analytics Platforms

Here are the most important elements that every rock-solid data analytics platform should have:

1. Intuitive design

The hallmark of a good user experience is an intuitive design that reduces the learning curve for the user. If users struggle to find essential features or get lost in a maze of menus, the platform hinders the effectiveness of data analysis.

In general, the fewer clicks that the user needs to get to the information they are looking for, the better the user experience. This means navigation menus should be clear and logically organized, filters should be placed close to the buttons or columns they’ll be used with most, and colors and images should be used to enhance user understanding of a specific tool or action.

2. Fast onboarding

User onboarding is a crucial moment in a product user journey since the quicker a user can start leveraging the product, the sooner they can see value from the investment. A good onboarding process should introduce key product features, guide them step-by-step through a suggested flow for their first projects, and provide opportunities for optional interactive tutorials or training sessions.

3. Easy access to contextual information and recommendations

While data analysis isn’t always straightforward, a good analytics platform can make understanding the data much easier through the integration of technology like Explainable AI (XAI). XAI guides users through the analysis of complex datasets, automatically providing context and next steps in simple, natural language. Virtualitics also leverages XAI to augment explanations and recommendations with compelling, AI-generated visualizations to help with data interpretation.

4. Clear and actionable visualizations

The human brain understands information better and faster when it’s presented in visual form. Whether it’s in-app guidance or 3D data visualizations for analysis, information should be delivered in a clear and uncluttered manner so users can quickly comprehend what they’re seeing and understand what actions to take next.

Clear labels, tooltips, and interactive features not only add to a more meaningful visual experience but also decrease the chances of data misinterpretation and incorrect decision-making.

5. Responsive and fast performance

Speed is a critical component of analytics product design. Slow loading, delayed responses to inputs, and sluggish data retrieval leads to frustration and tool abandonment. On the other hand, quick and smooth loading times for analysis, reports, and visualizations support real-time interaction with the data and maintain user engagement.

6. Robust customization options

Giving users the ability to tailor certain product features enhances the relevance and efficiency of their analysis. The right amount of control over templates and structures empowers users to align the tool to their unique analytical requirements and working style, providing a much more enjoyable experience.

7. Platform consistency

While customization is an essential part of good UX, locking down some features in favor of a consistent journey is also necessary. Providing the same interfaces to different user groups, such as in the reporting section of the platform, enables smoother collaboration and communication. It’s also especially useful when onboarding, as a consistent starting point can get all users up and running faster, and then, later on, the platform can recommend areas for them to dig deeper and customize their approach.

8. Seamless collaboration features

Users expect their analytics tool to have a collaborative environment for working with technical and non-technical stakeholders. Providing thoughtful features like shared dashboards, robust commenting tools, and multiple export options that cater to different data consumers can mean the difference between siloed, inaccurate data interpretation and a holistic understanding of the data across decision-makers.

A Smooth User Journey for Data Analysis

A strong UX greatly influences the success of data-driven decision-making and the adoption of AI applications. But it’s not just about making it easier for analysts to do intelligent data exploration and reporting. It’s also about enabling as many people within the organization to use the analytics platform consistently and with confidence.

To truly get the most out of the wealth of data that your business sits on, you need a broad and diverse group of stakeholders using a powerful AI platform. From highly skilled analysts to business managers getting their feet wet with data analysis, the more people leverage data analytics platforms in their decision-making, the more success your organization will have in optimizing its processes and developing strong, competitive strategies. 

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