The death of the dashboard: Prepare for a new era in enterprise intelligence

Dashboards have long been the go-to tool for enterprise data visualization. But let’s face it—they’re relics of a bygone era. Static, inflexible, and siloed dashboards struggle to keep up with the dynamism of today’s real-time data demands. Enterprise leaders are increasingly frustrated by their inability to turn insights into action at the speed business now requires.

The future of enterprise decision-making lies in a seismic shift—moving from dashboards to intelligent, real-time conversations with data to drive workflows. Powered by semantic data models, SQL’s enduring reliability, and large language models (LLMs), enterprises can finally break free from the constraints of dashboards to embrace proactive, AI-driven insights of the kind essential to embrace agentic workflows.

Our old friend SQL remains core in the post-dashboard age

Structured query language (SQL) has been the backbone of enterprise data management for decades. It’s a standardized programming language that manages and queries structured data in relational databases. Think of SQL as the universal translator for databases—its simplicity and versatility make it indispensable for integrating, analyzing, and visualizing data. Some tech comes and goes—but SQL has evolved to stay relevant, powering modern tools such as Snowflake, which layers semantic models on top of SQL to create a more intuitive way of interacting with data.

Semantic data models translate complex database structures into plain language concepts. This allows businesses to ask questions in natural terms—no advanced coding skills required. When paired with SQL, semantic layers unlock unprecedented data accessibility, paving the way for the next evolution in enterprise intelligence.

From static dashboards to natural language conversations with data

Semantic models—found to varying degrees in offerings including Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics, and SAP Datasphere—allow enterprises to move beyond static queries and visualizations. These models define relationships within the data, making it easy to ask real-time questions without needing an army of data analysts.

Enter LLMs such as OpenAI’s GPT, Google’s Gemini, and Inflection AI. These models simulate human-like reasoning, empowering businesses to interact with their data via natural language. LLMs make static dashboards redundant by delivering dynamic insights, tailored predictions, and contextual recommendations for action directly through conversational interfaces.

Natural language processing (NLP) turns human questions into actionable machine-readable commands. Imagine asking “What’s our current inventory position versus demand forecast?” and receiving a detailed, proactive response that includes not only the numbers but also suggestions for optimizing production schedules. No dashboards, no delays—just answers. Connect this with the rise of agentic AI to take action on those insights, and a new workflow emerges.

For a live example of an NLP interface with data, go and ask your questions of the research (data) on our website, here.

Why dashboards must die and what you must do to take advantage

Dashboards are designed for historical data snapshots. But in today’s hyper-competitive markets, you need a constant reading of the pulse of your operations. Static visualizations lack the depth, immediacy, and adaptability that modern business demands. For example, AI agents can alert supply chain managers to potential disruptions in real time and recommend solutions—actions dashboards simply cannot deliver.

To take advantage, you must:

• Invest in building semantic data relationships on SQL foundations
• Deploy LLMs and NLP interfaces to foster real-time, dynamic conversations with data
• Embrace a culture of agility and transparency to accelerate trust and adoption of AI-driven tools

It’s important to note that LLMs rely on clean, high-quality data. Enterprises must establish strong governance to avoid misleading outputs. To trust the insights, you must build confidence through explainable models open to scrutiny. And shifting from dashboards to conversational data tools requires you to get comfortable with new ways of working.

The HFS Data Cycle remains as relevant as ever to your data strategy

The HFS Data Cycle (see Exhibit 1) was first conceived in 2021. Despite the paradigm shift we describe in the death of the dashboard, it remains your framework for thinking through changes in data strategy.

The first step in your data cycle must always be obtaining the data required to win in your market. Semantic layers add potentially better-performing sources for that data. In step two, you must include the possibility of LLMs and agentic AI when you rethink your processes. In step three, include GenAI in how you design your new operational flows in the cloud before step four, where you automate as many rethought processes as possible. GenAI drives and derives insight in step five, and NLP makes this insight more universally accessible within the enterprise. Within human-set strategic guidelines and increasingly in agentic workflows, the outcomes feed back into step one to identify how and where to source the data to win in your market, and the cycle repeats.

Exhibit 1: Update your options for getting the best data to win in your market, rethink processes, and understand the democratization of access to insight headed your way

Source: HFS Research, 2024

Prepare for the next evolution in how work gets down

The shift from static dashboards to a connected, contextual, and creative approach to enterprise workflows represents the next evolution of how work gets done. GenAI is the catalyst in conversational interfaces and fundamentally changes backend operations to deliver proactive insights and autonomous actions.

We should not have to spend our days endlessly querying for insights to prioritize our next actions. Instead, imagine a system that doesn’t wait for an underwriter to query which cases are most profitable but synthesizes hundreds of documents in real time, compiles a comprehensive case packet, and surfaces the top opportunities directly to them—enhanced with predictive analytics and actionable recommendations.

This is the reinvention of the dashboard as an intelligent command center, triggering workflows, automating approvals, and driving decisions without human intervention. The data cycle becomes a dynamic, real-time loop that combines semantic relationships, LLM-powered cognition, and enterprise workflows to create value at the speed of need.

The Bottom Line: Rethink your data strategy to align with the speed and complexity of modern markets.

The death of the static dashboard as we know it is not just inevitable—it’s necessary. You must rethink your data strategy to align with the speed and complexity of modern markets. Businesses can transform how they make decisions by adopting semantic data models, SQL-powered infrastructures, and AI-driven agents.

Dashboards served us well in the age of static reporting. But the age of real-time, dynamic data interaction is here. Enterprise leaders that act now will lead in the dynamic data-driven future. The question isn’t whether dashboards will die—it’s how you will prepare your organization to thrive in their absence.