Why are most dashboards ultimately useless?In many companies, data teams keep producing more and more dashboards. Yet they rarely influence real decisions.The problem is not the quality of the data or the BI tools.The problem is that most dashboards were designed to observe, not to act.In this article, I explain why it is becoming necessary to move from traditional Business Intelligence to “Decision Products”: data products designed to trigger concrete actions within business processes.I also share several examples of truly actionable KPIs in e-commerce, marketing, customer relationship management, and supply chain contexts.The goal is no longer simply to visualize performance, but to connect analytics, data platforms, and operational decisions.Full article below.
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For more than two decades, Business Intelligence (BI) has promised to transform organizations into data-driven companies. Massive investments have been made in:
Yet in many organizations, dashboards rarely influence real decisions.
They are often consulted during reporting meetings or monthly reviews, but they rarely trigger operational actions.
The issue is not the technology.
The issue is how analytics is designed.
Traditional BI produces visualizations.
Modern organizations need decision products.
A decision product is a data product designed to trigger or guide a business decision, not simply display information.
Instead of answering only:
What happened?
Decision products help answer:
This approach is closely related to the emerging discipline of decision intelligence, which combines analytics, AI and decision modeling to transform data insights into actionable decisions.
Many dashboards follow the same implementation pattern.
Technically everything works.
Operationally, however, three structural problems appear.
A typical dashboard contains 20–50 KPIs.
Humans cannot quickly interpret that many signals simultaneously.
The result is analysis paralysis.
Most dashboards focus on descriptive metrics such as:
These indicators describe what happened, but not what should be done next.
Dashboards are usually disconnected from operational workflows.
Typical examples:
As a result:
teams observe data but do not act on it.
Analytics capabilities have evolved through several stages.
Reporting
Objective: visualize data
Outcome: understand what happened
Business Intelligence (BI)
Objective: analyze performance
Outcome: explain trends
Advanced Analytics
Objective: predict outcomes
Outcome: anticipate future events
Decision Products
Objective: trigger actions
Outcome: enable operational decisions
Traditional BI focuses on visibility and reporting, whereas modern decision-centric analytics focuses on action and decision flows.
The difference between dashboards and decision products becomes clearer through examples.
Traditional dashboard metrics:
Decision product metric:
Products with demand exceeding inventory by more than three days
Possible actions:
Traditional dashboard metrics:
Decision product metric:
Campaigns with acquisition cost 30% higher than target
Actions:
Traditional dashboard metrics:
Decision product metric:
Customers with churn probability above 70%
Actions:
Traditional dashboard metrics:
Decision product metric:
Products likely to experience stock-outs within five days
Actions:
Designing a decision product requires a different approach to analytics.
Instead of asking:
Which KPIs should we track?
Ask:
Which decision are we trying to improve?
Every decision occurs in a specific operational context:
The analytics product must integrate into that moment.
A good decision product usually contains:
Nothing more.
Modern analytics systems increasingly integrate:
These capabilities allow organizations to move from data visibility to decision automation.
Artificial intelligence dramatically increases the potential of decision-centric analytics.
AI systems can:
However, their value emerges only when predictions are connected to decisions.
Otherwise, AI becomes just another analytical layer.
Decision intelligence frameworks address this gap by linking analytics outputs directly to operational decision processes.
Most dashboards fail for a simple reason:
They were designed to observe performance, not to drive decisions.
The next evolution of analytics is not another generation of dashboards.
It is the emergence of decision products:
Organizations that succeed with data will not be those producing the most dashboards.
They will be those building the best decision products.
Axel Douchin is a Cloud, Data, and Artificial Intelligence executive serving as an interim CIO, CTO, and Chief Data Officer for organizations facing complex digital transformations. With more than 20 years of international experience, he helps companies design scalable cloud architectures, modern data platforms, and AI-driven decision systems that connect analytics to real operational impact.
His work focuses on:
More insights on Cloud, Data, and AI strategies:
https://www.douchinconsulting.com
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