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Why is user experience important for dashboard performance

With digital transformation gaining traction within companies, managers and business analysts now have a multitude of solutions at their disposal to help them optimize and organize their work.

Among them are the so-called Business Intelligence tools, through which it is possible to create personalized dashboards.

These dashboards allow stakeholders quick access and visualization of operational and institutional data, eliminating the need to accumulate numbers and information in infinite spreadsheets or databases.

However, despite the gains that operational teams can achieve with such technologies, the main challenge that these same institutions currently face is to find relevant data that is actually relevant for decision making in the midst of an enormous volume of information.

User Experience and Business Intelligence Tools

Most analytics tools today are focused on what has been called “self-service BI”, through which users can create their own analysis dashboards with charts and indicators.

On the surface it may look simple, but in practice adapting to this type of functionality can create barriers and in some cases even discourage the use of technology, especially due to the availability of a large amount of information and data without any type of visual organization for the end-user.

In turn, this can become a nightmare for those who don’t have the time or skills to filter and visualize the information that is relevant to them.

To solve this, we need work to visually organize the data. And so to squeeze it’s full potential, we need to go through a series of steps and the selection and arrangement of information becomes fundamental to achieve a good Analytics User Experience.

What makes a good user experience?

Before implementing any BI dashboard at the company, it is necessary to understand the profile of who will be using this tool. We achieve this with one or several briefing meetings.

Once that information is gathered the second phase involves building a prototype using wireframes – a screen layout in which the user can see the final interface of the tool and how the information will be arranged in it.

For traditional Business Intelligence implementation projects, prototyping is usually responsible for about 50% of the work as it relates to the visual layer. This is done in order to avoid frustrations regarding the deliverables – and, consequently, avoiding tedious (and expensive!) rework.

The important thing is to follow each phase in the construction of the BI based on this triad: type of information, main goal and audience. From this premise we’ll define the most suitable resources needed for completion of the company’s Business Intelligence tool.

By identifying the users’ profile, their expectations and prototyping based on these premises, we can see that a guided navigation, with pre-defined analyses, will be most useful for them and the tool’s usefulness for non-technical profiles. Especially if they are new to BI in general.

Likewise, if the users’ profile is more inquisitive, as is the case with analysts, a “self-service” tool is possibly the most suitable, as it will allow any business question to be answered quickly.

How do you build a solid UX experience for Analytics?

The word “User Experience” goes beyond the idea of how users will navigate the interface of a BI tool, or what the “look and feel” of data analysis will be like.

On the contrary, the real Analytics User Experience has to do with being able to handle and work with the most relevant information, as quickly and clearly as possible.

Designers and developers are responsible for delivering this ready-to-use, personalized tool that is easy and intuitive to work with, and ready to respond to any stated demand that was previously identified as a goal.

In this way, considering user experience when building BI delivers more valuable business insights.

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