A Quick Guide to Designing Data Intensive Applications

Understand applications better, get a glance at the possible challenges and setbacks, and the best practices when dealing with data-heavy apps.

In the modern smart technology landscape, organizations are constantly trying to provide their customers and end-users with intelligent and seamless experiences. Modern-day applications reside on cloud, and generate tons of data that can be used to derive useful insights and patterns that can help key stakeholders take faster and more informed decisions. But first the data needs to be aggregated, processed and presented in a format that is easy to consume. Applications can help process data and generate useful information in a matter of milliseconds.

However, some industries and domains demand for robust applications that can withstand, assimilate and process data within seconds. These include the Banking and Financial Services, Data Science, Data Mining, Digital Marketing, and IoT to name a few. These types of data-heavy applications typically have a high number of users, large amount of data, a high number of interactions such as incoming data from third party applications or sources, telemetry data, aggregations or selective queries.

Usually, these data-heavy platforms would fall under any one of these:

• Apps with a complex UI and high amount of data with heavy features
• Apps with simple UI but with a large number of features
• Apps with complicated data-heavy backends

Designing and maintaining such data-intensive apps without burdening users with scattered, incomprehensible information is a real challenge.

Demystifying the Challenges in Data-intensive Apps

Building, designing, and supporting data-intensive apps can also give rise to some unique challenges. Let’s take a look at the common challenges faced while designing for data-heavy apps in the UX product planning phase:

• Overwhelming visualization with a lack of clarity – Sometimes all that an application might need is more focus on primary tasks and interactions instead of fancy graphical elements, which can be distracting and leave users confused. Rather, information should be presented in a way that critical/general information and CTAs are clearly distinguishable.
• Designing for a one-size-fits-all approach – An application might have multiple users. While some might be interested in a high level summary, others might need a deep dive into the data. This requires a clear demarcation between users, their access and rights, respective workflows and the features and tasks that should be available.
• Planning for scalability – When planning for new product features, improvisation plans on the existing ones should be thorough. Existing apps may need consistency and standardization while new features may need to be fine-tuned. New product features should be aligned such that the look, feel and interaction of the app provides a seamless experience for end-users .
  Agile sprints can also be a good way of planning new product feature releases. Each sprint may have a parallel task of standardizing the current UI. This planning needs to be agreed upon and signed off by product teams, development teams and business stakeholders.
• Data representation – Information should have a proper hierarchy, i.e. proper visual hierarchy should be planned so that users can easily delve deep into data as and when required. For example, in dashboards and visualization graphs, the emphasis should be on selecting the right format as per the variables. This may also include features or UI options on these data visualizations which will take the user to the detailed view of the data if he wants to view it. As simple as it may be, at times raw data in MS Excel sheets can serve the purpose too.

Adopting Best Practices

Take a look at some of the best practices that you can follow while designing data-intensive applications:

• Detailed user research and persona mapping

Firstly, you need to identify different user personas and chart out their needs through workflows and journeys. Here are some of the questions to consider in the user experience (UX) product planning phase.

• What are the user’s primary and secondary tasks?
• What are the user’s metrics of successes/failures while interacting with system?
• How do the requirements vary for different user roles?
• Which piece of data is crucial for storytelling?
• What will the users be using the data for?

• Usability and clarity are key

Users can get easily lost in an app which has a beautiful design but lacks clarity and usability.  For instance, bars could be good in plotting categorical data, but could defeat the purpose in displaying quantitative data in intervals. In some cases, users can leverage text and visual for better clarity. Important points to consider here are as follows:

• How should data be represented?
• Is data representation easy enough to comprehend a logical data flow
• Does my user understand how to reverse or figure out what are the next steps?
• Are users able to track their actions especially in complex environments where data analysis are lengthy?
• Is it easy to transition between multiple workspaces and tools while using your application?
• Is an important piece of information easily noticeable?

•  Technical feasibility and implementation

Amidst all fancy design proposals, implementation is the crucial key to consider.

• How is the data rendering in the front end via the back end? What kind of data visualization is needed?
• How can user experience be enhanced while day-to-day activities run smoothly?
• How can you keep the implementation of the UI simple? The use of standard systems like material design or MUI may be recommended to keep the UI consistent and save development time.
• What is technically possible and what is not?
• Does my design allow room for improvement as per user feedback?

• An enhanced experience

As designers, you need to think about how to strike the right balance between data and communication, especially when creating the look and feel of the dashboards. Users in control shouldn’t feel overwhelmed, confused or distracted by the interface. Here are some pointers to consider while designing a data-heavy UI:

• How can you design information architecture (IA) that makes sense to the user and allow them to feel comfortable when interacting with the application?
• The users for these types of apps are generally expert users, so how do you save their time and help them complete their tasks quicker?
• How can you enhance interactive visualization, especially with real time data in complex applications?

Raising the Bar of Excellence in UI/UX

Customers building data-intensive applications should be focused on the UX design right from the start. This avoids going back to the whiteboard for redesign exercises later. Using standard components via design systems leads to cohesion across the applications. With the UX team onboarded from the beginning, the app has a robust foundation for the features and additions that may come later in the product lifecycle. Further, it makes the system scalable, robust, efficient and available.

UX Expertise at GS LAB

At GS Lab, we believe UX is the core of building user-friendly digital products with a perfect balance between branding and customer experience. Our UX team is focused on changing trends, technologies and human behaviour to ensure we are at the forefront of dynamic new opportunities to craft and deliver a better UI/UX for our customers.
We have worked with multiple start-ups and established clients on data-heavy applications, where our teams have been instrumental in establishing design standards, handholding our client teams and developing products through multiple iterations and life cycles.

For more information about our cutting-edge UX projects and how we can help your business develop a dynamically modern UI/UX, read more at https://www.gslab.com/user-experience-design