The three types of analytics dashboards.

Dashboards are often treated as a single category of tool, but they serve very different purposes depending on the user.

A customer viewing analytics inside a software product, a CEO reviewing company performance, and a data analyst exploring a dataset may all be looking at charts, but they are usually using very different types of dashboards.

The right approach depends on who needs the information, how much flexibility they need, and how closely they need to work with the underlying data.

Embedded dashboards are dashboards built directly into a product or application.

A simple example is a financial platform showing a customer their revenue over time after they log in. The dashboard is part of the product experience rather than a separate analytics tool.

These dashboards are typically built by software engineers and designed around a specific user experience. The organization controls the layout, branding, and available functionality, while the person viewing it usually has limited ability to customize what they see.

This makes embedded dashboards a strong choice for customer-facing analytics where consistency and simplicity matter.

Business intelligence (BI) platforms are the dashboards most organizations are familiar with.

Tools such as Tableau, Power BI, Metabase, and even Excel are commonly used by internal teams to bring together information from multiple systems and create reports around business performance.

A leadership dashboard might show revenue growth, customer acquisition, operational metrics, or supply chain performance. The goal is to organize the data so people can understand what is happening across the business, rather than simply display it.

The dashboard itself is usually the final layer of a larger data process. Much of the work happens before anyone opens the dashboard.

Preparing the data, defining metrics, and deciding how different sources should be combined all happen upstream.

Modern data platforms such as Databricks and Snowflake also provide dashboarding capabilities directly on top of the data environment.

The main advantage is that these dashboards sit close to the underlying data. Teams do not need to move information into a separate reporting system before analyzing it, which can reduce engineering effort.

These dashboards are particularly useful for data teams working close to the data itself. They support governed metrics, exploration, and workflows where analysts need to move quickly while staying within defined data structures and permissions.

For example, a team may want to analyze revenue while ensuring that the definitions, available datasets, and access rules are controlled. Features such as Databricks Genie build on this approach by allowing users to interact with governed data using natural language.

There is no single best dashboard technology.

The right choice depends on the problem being solved and the people using it.

If the goal is to provide analytics directly to customers, an embedded dashboard usually makes the most sense because the experience can be tightly controlled.

If the goal is helping leadership and internal teams understand business performance, BI platforms provide the flexibility needed to combine metrics and build reporting around business needs.

If the users are analysts or technical teams working directly with the data environment, dashboards inside the data platform can reduce complexity and keep analytics close to the source.

The important decision is not choosing the tool with the most features. It is choosing the approach that matches the users, the workflow, and the type of questions the data needs to answer.