4 Jul 2022| ONPASSIVE
Factors to Consider Before Designing Big Data Visualization
Big data visualization is the simplest, convenient and interactive technique to illustrate the connection within data. Traditionally, businesses used graphs, bars and charts to present relationship in data. However, when it comes to large volume of data or big data, traditional techniques do not fulfil the requirements and mostly fail to show this relationship.
Big data visualization imparts an artistic visual representation of given information, leveraging more interactive and animated illustrations to establish connections among massive data with better user understanding. Now let us understand what UX designers have to consider before the implementation of big data visualization.
Factors to be Considered before Implementation of Big Data Visualization
Comprehend the Problem
Before providing any solution to a problem, it is necessary to gain in-depth knowledge about the problem in the first place. This will allow you to come up with the most accurate solution.
Big data visualization allows users to understand big data in the simplest way possible and analyze it to make better decisions. And to achieve this, the UX designers must understand the problem that needs to be resolved using visualization. The designers must try to dig out what the user wants to see and examine from visualization.
Defining the problem will help the UX designers to create the most appropriate big data visualization, helping users to achieve their goals.
Know Your Users
Once you get an insight of the problem, its time to find out about the users who are likely to use the designed visualization. Knowing your users will help you to understand their level of proficiency and comprehension of advanced visualizations. The UX designers carry out a thorough research to identify the users.
These users and their behavioral patterns are observed and analyzed by the designers, which will allow them to understand the goals they want to achieve from the solution. User involvement is an important aspect to start with big data designing.
Overall, a deeper understanding of different users, their behaviors and goals will guide the UX designers to develop a better solution that will improve user experience.
Select Appropriate Visualization Type
The visualization fulfils its purpose only when the users can easily understand the pattern and trends in the big data. Since each visualization has its own purpose and usage guidelines, it is of utmost importance to select the right visualization type depending upon the available data and user understanding. A UX designer must have a piece of adequate knowledge about the changing trends in visualization approaches.
Factors to be considered by UX designers before the selection of Visualization type:
Scatter plots, Polar area diagrams, Time series sequences, Tree diagrams, Ring charts, Sunburst diagrams, Density map and Cartogram are some standard big data visualization types.
Include Relevant Information
What happens when a lot of information, irrespective of its importance, is provided to us? It gets a bit tricky for us to understand the data. The same happens when irrelevant data is included in the visualization. It will clutter the view and make it difficult for the users to comprehend the relationship within the data. Here, the UX designer’s responsibility is to identify data that are less important or least likely to impact decision making, and eliminate them.
Meanwhile, the most valuable data must be highlighted and given a prominent space on the visualization. This will make it convenient for the users to interact and analyze the data, and make better decisions.
Visualization technique serves the purpose to convert big data into visual representation, making it easier for the users to comprehend it in the simplest way possible. Being interactive in nature, big data visualization enables the users to filter and navigate the data for better understanding. Prior to implementing the visualization, UX designers must understand the users and their problems, and come up with the best visualization type matching the available data. Considering these practices will improve the big data visualization experience of the users.
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