Dashboards: Dead or Alive? The evolution from data graveyards into data gold mines

August 17, 2023 | 6 minute read
Barry Mostert
Senior Director, Oracle Analytics
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Recently I’ve seen the subject of analytics dashboards reaching the end of their usefulness raised on more than one occasion, whether in a live in-person event or in a social post. But is this really the case? Are dashboards indeed an outdated form of presenting data to stakeholders and making decisions?

Are dashboards really dead?

What are traditional BI dashboards?

Classic dashboards have been around for decades, intended to empower business users with data-driven insights without the need for technical skills. When I began my analytics career nearly 20 years ago, "dashboards" were already prevalent. They served to simplify data access, letting executives explore key metrics without knowledge of the underlying data architecture.

Building those early dashboards was no simple task; it required deep expertise across data visualization tools, storage systems, and accessing and transforming data into business concepts. While the creation was complex, the consumption was simplified. Users could understand and action insights.

Traditional dashboards removed the complexity of data for businesspeople. But creating them still demanded technical specialization. These static, legacy dashboards remain common today despite lacking modern flexibility and intelligence. The next generation of analytics must balance technical depth with intuitive business exploration. Insights for all, complexity for none.

Oracle Business Intelligence Enterprise Edition traditional dashboard example
Figure 1: A traditional dashboard example in Oracle Business Intelligence Enterprise Edition

What's the problem with traditional BI dashboards?

Legacy dashboards often rely on dated technologies and methods, lacking capabilities such as natural language, ML predictions, and built-in augmented data preparation. Developers are resistant to adopt (or adapt to) emerging innovations. The old "if it ain't broke, don't fix it" mentality has many organizations still using old approaches that lag behind current best practices.

For example, traditional dashboards struggled with real-time analytics, taking minutes or hours to refresh. Interactivity was limited too. Some allowed basic filtering or drill-down within preset data sources, but venturing outside the developer's predefined lanes was impossible.

When people say things such as “dashboards are where good data goes to die,” they're typically referring to the traditional type of dashboards. The data presented on these dashboards often becomes stagnant and isn't actively used for decision-making, gradually diminishing its value over time. These dashboards are generally constructed and maintained centrally by IT. Making changes can be challenging, and the process of creating dashboards is often rigorous and time-consuming. As business needs evolved, many dashboards were created but rarely removed. Consequently, organizations have been amassing huge repositories of thousands of inactive dashboards. Business users perceive little value in them, often turning to alternate visualization tools or even resorting back to spreadsheet-based solutions instead. Sound familiar?

This type of traditional dashboard is what Gartner refers to as “mode 1” business intelligence (BI). BI that was created centrally by a dedicated team, posted to a repository for business users and left there for the rest of eternity.

How are modern dashboards different?

A modern analytics dashboard (which leverages cloud technology) offers enhanced accessibility, real-time interactivity, user-accessible data integration, and collaborative capabilities that distinguish it from the older style of IT-led BI dashboards prevalent in legacy on-premises tools. Modern dashboards excel on two fronts compared to traditional, IT-led approaches. First, embedded AI/ML augments human analysis with machine intelligence. Second, self-service analytics empowers business users with autonomy.

AI and ML boost productivity by automating tedious data preparation and enrichment tasks. They also detect hidden insights such as anomalies, unseen patterns, and future predictions. This enhances data stories with machine learning, minus the manual effort.

However, technology alone isn't the answer. True enablement comes by putting analytics directly into the hands of business users by fostering a culture of data literacy and by providing intuitive tools for self-service exploration and decision-making. Self-service removes reliance on IT or specialists to build reports. Now cross-functional users can freely access, analyze, and share data on their own. With IT empowering through governance, not limiting through control, the possibilities are endless. People ask their own questions, find their own answers, and tell impactful data stories that positively affect the business without bottlenecks.

The future of dashboards is democratization, not restriction. Blending embedded AI/ML with self-service exploration makes modern dashboards a portal into limitless insights.

How has Oracle Analytics evolved?

Previous versions of Oracle Analytics (such as OBIEE) were designed for centralized, IT-controlled business intelligence. The semantic model simplified access to complex data architectures, transforming them into business metrics.  But there were drawbacks, such as IT becoming a bottleneck trying to keep up with business requests. Now with the option to upgrade to Oracle Analytics Server (OAS) on-premises or hosted in cloud, OBIEE customers gain the opportunity to give businesspeople the modern analytics tools and interfaces that their users demand while still retaining their investment in the corporate semantic model.

Today's Oracle Analytics platform (both Oracle Analytics Cloud and Oracle Analytics Server) has expanded into self-service for business users. Now cross-functional teams can easily obtain insights without relying on specialists. AI and ML augment human analysis with recommendations and productivity enhancers such as generative AI chat interfaces.

Oracle Analytics platform YouTube product tour
Figure 2: Oracle Analytics platform YouTube product tour

IT retains governance for compliance and reliability. But the focus has shifted from centralized reporting to decentralized self-sufficiency. User autonomy, not technical complexity, now sits at the core. By combining governance with flexibility, Oracle enables actionable analytics. People ask their own questions and find answers quickly. Backed by machine learning, human intelligence scales further. Oracle Analytics allows organizations to become truly insights-driven by making analytics accessible, intelligent, and empower users to be self-sufficient.

Oracle Analtyics Cloud with live interactive data visualizations embedded into a slide. Live visualizations allow people to focus on the results and making the right decision.
Figure 3: Oracle Analtyics Cloud with live interactive data visualizations embedded into a slide. Live visualizations allow people to focus on the results and making the right decision.

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Sure, there are most certainly many examples of BI or analytics projects that still have traditional style centrally created static dashboards where data indeed goes to die. But hopefully it’s clearer that all dashboards aren’t created equal, and the same fate doesn't apply to modern dashboards that are designed for self-service and that use AI/ML throughout.

For clarity, data doesn’t die and dashboards aren't dead. But traditional mode 1 style BI can stagnate data, making it less useful and untrustworthy. Business users and executive stakeholders that aren’t savvy enough to connect to and retrieve the data that they need will always rely on some form of dashboard. In the absence of a good solution, they'll resort to spreadsheets – and no one wants that.

My recommendation is to stop building old-school centralized dashboards (also known as mode 1) that are used briefly and then forgotten. Instead empower users with self-service and modern technologies (such as AI/ML) to answer their own questions, tell their own stories, and make better more analytics-driven decisions (also known as mode 2).

Read more about Gartner’s mode 1 versus mode 2 here

Read more about the differences between data-driven, insights-driven, and analytics-driven organizations here

Take the Oracle Analytics video product tour to learn more

Barry Mostert

Senior Director, Oracle Analytics

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