Dashboards face a bigger challenge than UI/UX, it’s infrastructure

Not UI-UX, infrastructure is the real challenge in dashboards

For successful dashboards, we must look beyond UI-UX and address the underlying data infrastructure challenges.


What goes into the making of a great house?

While the exterior and interior design is the most visible aspects, there are far more critical aspects to consider like selecting the right site for construction, creating a robust architecture plan, and so on.

Once all of this is in place, aspects related to the design (like door and window patterns, color and texture, woodwork, interior decor, etc.) definitely add to the aesthetic and the quality of living. 

The same is the case with data visualization, and in particular, dashboards. But because of their deeply visual nature, it is easy to fixate on the UI-UX aspect of dashboards. In reality, the design, user experience, and overall look and feel of data dashboards merely constitute the tip of the iceberg.

In this blog, we share with you the core data infrastructure elements that go into making dashboards that perform and excel. Let’s get down to it right away!


#1 Ingesting data in multiple forms from diverse & disparate sources

We live in a richly interconnected world. Accurately representing the real-world truth in a dashboard rarely happens by relying on just one kind of data or a single source. A 2021 study by IDC, for example, shows that 79% of organizations use more than 100 data sources, and the top 30% use more than 1,000 to conduct digital business.

In sheer numbers alone, this poses significant infrastructure challenges for the seamless ingestion of data. But the problem is further compounded by the fact that data is rarely available in a uniform format all across. Instead, datasets vary widely in terms of how structured they are (i.e. structured, semi-structured, unstructured, and poly-structured), and the data type they belong to (i.e. numbers, text, images, etc.)

Dashboards, therefore, need to have a versatile and agile ingestion module that can easily connect with any number of disparate sources to ingest heterogeneous data effortlessly.  

#2 Integrating high-volume, complex data in an automated manner

In data analytics, poor quality of incoming data inevitably leads to faulty outcome streams. No matter how sophisticated the analytics and visualization modules are, if the influx of input data streams is not cleaned, transformed, and integrated, the end result will be hollow charts and faulty insights

But that is not all. Since data dashboards are visual and intuitive, the entire data integration process must happen automatically without any user intervention. Otherwise, they will consume so much time, engineering effort, and financial resources that they will cease to remain viable and useful.

According to this article on Forbes, more than 80% of enterprise Business Operations leaders say that data integration is critical to ongoing operations. For dashboards to create sustained, meaningful, and purpose-driven decision intelligence, they must have an excellent capability to integrate data at the pace of generation. This also needs to happen at the industry scale and in the near-real-time.

#3 Creating and managing the data pipeline in an end-to-end fashion

In dashboards, the vast majority of DevOps and DataOps challenges revolve around effective pipeline management. Data pipelines play the mission-critical role of extracting, transforming, validating, and loading the data to and from one point to another in the data lifecycle. This seamless data transportation powers everything that goes on behind the scenes in a dashboard.

Pipeline management is made further complicated by the fact that each data user works with the same data in a different way. The variation in data purpose and data operations means that it becomes much harder to implement standardization rules on the data pipeline.

Furthermore, data pipelines need to be reusable and repeatable so that the overall data engine is efficient and synchronized at all times. Right from data origination to data visualization, the pipeline must function in a clean and streamlined way.

Verb’s automated Infrastructure from orchestrating queries to providing exceptional customer-facing dashboard experience

#4 Blending together various tools & technologies for a unified experience

Unless you are using an all-in-one customer dashboarding tool like Verb, your software development team likely feels the need to constantly toggle between a wide array of tools across the data lifecycle.

Much of dashboard building happens through a process that is similar to that of assembling the various components of a mobile phone. Just like a group of engineers put together the various parts of a mobile phone like the circuit board, microphone, display, speaker, sim card holder, buzzer, vibrator, antenna, etc., the same needs to be done with the tools that fuel the various data operations.

It is no surprise, therefore, that the final dashboard experience often feels a lot like a hotch-potch of multiple data environments that don’t sit well together. Successful data dashboards need to tackle this problem at the roots to craft user experiences that are integrated, unified, and harmonious.

Great dashboards have to grapple with multiple layers of data complexity and infrastructure issues. From our experience of serving leading B2B SaaS companies with excellence in no-code customer dashboard solutions, we can tell you this: successful customer dashboards are seldom built upon UI-UX alone. They rely heavily on back-end infrastructure elements and how the issues therein are resolved.

Interested to explore how Verb can help you build robust customer data dashboards for your B2B SaaS company from the ground up?

If you want a tour of our newest features get in touch!