Are analytics dashboards dead? Here's how to bring them back to life

Analytics dashboards haven't really evolved, but there are ways to help them become more useful to end users.

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The analytics dashboard initially appeared in the 1980s as part of Executive Information Systems (EIS), so dashboards are anything but new. But are they beginning to outlive their usefulness? 

"We've hit a wall now with dashboards, and there are three areas where I think we've reached a limit in their usefulness," said Glen Rabie, CEO at Yellowfin, a business intelligence (BI) platform. "Although dashboards have been around for well over 30 years, they haven't really evolved or matured enough to address a fundamental limitation," that most dashboards don't drive action. 

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Instead, users employ dashboards to browse metrics for a summary overview, with the bulk of the analytics workload depending upon how far the user wants to dig for answers. Exercises like this rarely result in deep or actionable insights.

"People don't have the time or inclination to search for hidden insights, and the data in their dashboards often becomes old, inaccurate and not necessarily built for or aligned with what the user is trying to do," Rabie said.

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A second issue is understanding the business context behind the data. A user who is new to the business could see a change in the data and not understand the implications of it. The user must then seek feedback from others so connections between the data and what's happening in the business can be made.

"Without a purpose for the visualizations, the data story can't be fully developed, and it can lead to erroneous data. It also leads to improper design and functionality as it isn't tailored enough to the needs of the user," said Nicolo Palos, an IT marketer and blogger. 

New analytics developments 

To address this, there are four new developments in analytics that can augment the dashboard experience to make it more valuable. 

1. Automated analysis

Automated analysis is also called automated business monitoring. Instead of requiring users to manually uncover hidden changes in their dashboard data, automated analysis can automatically identify and surface those critical changes faster than a user can manually uncover them. New tools issue automatic alerts that identify the changes. 

2. Assisted insights

"This lets people find out why changes happened by using (artificial intelligence) AI to run analysis algorithms to find the root cause," Rabie said. You don't need a data analyst to do this. Instead, the entire process is machine-generated within your dashboard. 

3. Guide the user to take action

This is achieved with data science models that can be embedded into a dashboard that trigger workflows. These models run algorithms over data to suggest to the user what best action to take.

4. Long-form narrative

Long-form narrative can be done via data stories or data-led presentations. "These combine the data charts and visualizations with contextual narrative, written by a business expert, to explain exactly what happened and why," Rabie said.

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How to improve the dashboard experience

Collectively, additional dashboard analytics tools like these can enrich dashboards. Here are four guidelines for a successful implementation.

1. Plan for the behaviors you want to drive in your business

Don't just think about data as the end-game of the dashboard; think about what you want people to do as a result of using that dashboard. For instance, if the user sees an alert about an imminent machine failure on a manufacturing assembly line, what should they do first? Order a new part, or conduct an in-person inspection?

2. Familiarize end users with the usage and benefits of enhanced dashboards

What new things can users do with these enhanced dashboards that they couldn't do before? Users need training and practice with new tools before they can get the most value out of them.

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3. Create more highly focused dashboards that drive specific operational outcomes

Dashboards that are highly focused on specific areas of the business (e.g. facility energy usage, on-time statistics for delivery routes, etc.) help focus users on those specific initiatives. Dashboards that are overly general can place too much burden on users to extract actionable information for the business.

4. Use data storytelling

Encourage experienced business users to share their own stories about how they use data to make critical business decisions. This is a great way to demonstrate real-world examples of analytics-driven decision-making to newer users and to embed the value of dashboards analytics in your culture.

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