Have BI Dashboards Outlived Their Usefulness?

Part of making efficient and effective data-driven decisions in the workplace is giving decision-makers timely access to information they can use to influence outcomes. Traditionally this has meant gathering relevant metrics into one convenient area so users can view data insights at a glance rather than having to dig for them each individually.

Dashboards have served this purpose, aggregating key performance indicators in one location so users could get an overview of whatever metrics had been deemed relevant to their job’s purview.

But does this approach really cut it in today’s increasingly competitive, fast-moving and data-driven workplace? Have BI dashboards finally outlived their usefulness? Let’s take a closer look.


Limitations of Traditional BI Dashboards

We’ve covered some of the reasons why the business intelligence dashboards have remained a mainstay for decades, namely convenience and information at a glance. That being said, there are plenty of limitations to consider as well, such as:


  • Traditional dashboards often provided only a high-level overview without offering users the opportunity to dig deeper into insights.
  • Pre-configured dashboards can gradually (or suddenly) fall out of alignment with more current business needs.
  • BI dashboards often tell “what” is happening but offer little insight into “why.”
  • Traditional analytics dashboards have lacked the level of customizability individual users have needed if they were designed for a broader audience.


At worst, users limited by traditional BI dashboards may end up drawing inaccurate conclusions based on the data provided due to the lack of drill-down capability and personalization to their specific needs. In other words, a dashboard full of metrics lacking context may actually drive counterproductive decision-making if users aren’t able to understand and incorporate important perspective. 


Outdated dashboards may also end up pushing users to work toward outdated or extraneous business goals if they are not able to update the dashboards to reflect more worthwhile objectives over time. In other words, dashboards may start to drive perceived business priorities rather than the goals dictating the dashboards.


Another potential pitfall associated with traditional BI dashboards is their tendency to include too much information beyond what is really useful — potentially leading to a confusing, cluttered landscape through which users must navigate to find what they actually need.


What Advanced BI Dashboards Are Bringing to the Table

An advanced BI dashboard, on the other hand, builds on these shortcomings while still consolidating data into a useful collection. Self-service analytics dashboards today can include features like “pull-down menus and intuitive drill-down points” so users can not only look at data insights but explore and transform them as needed, as CIO writes.


Self-service BI platforms also prioritize a personalized experience for users in all roles, allowing them to make dashboards and pinboards as needed, like when business goals shift or when new projects or questions come to light. Rather than having to make do with relatively fixed dashboards, users can create their own using the interactive charts and graphs that are automatically generated based on their queries — and embed their dashboards into other business applications, too.

So, have BI dashboards outlived their usefulness then? 

Yes and no.

It really depends on the features of the dashboard in question.

Traditional dashboards with limited customizability and drill-down capabilities are no longer equipped to fuel truly nuanced decision-making by a variety of users. Advanced BI dashboards address these limitations by allowing users to personalize and embed dashboards into collaborative workflows, as well as explore insights deeper than first meets the eye by clicking to delve into insights. It’s fair to say the former have surpassed their utility while the latter are still integral tools for the modern data-driven organization.

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  1. Nice Blog Post. Conversational analytics refers to the process of analyzing and deriving insights from conversational data, such as spoken interactions, text-based messages, chatbot interactions, or customer support conversations.