Modern Business Intelligence (BI) platforms have served as the backbone of data analytics for decades. Today, every organization strives to enable informed business decisions by making data available to more people. As data grows in volume and variety, these platforms and the teams who support and use them face significant hurdles to organize, manage, and access data consistently.

While modern platforms have been instrumental in data democratization by prioritizing self-service and ease-of-use, they often fall short of handling the complexity of today’s modern data stack. From cloud data warehouses and cloud-native enterprise applications to acquiring the right data and the joins, relationships, and calculations to correctly define a data model, there’s still too much guesswork for data analysts and business users who are seeking insights.

In addition, people must navigate the proliferation of duplicate dashboards and datasets, fragmented business logic, and inconsistent calculation of key metrics—all eroding trust in the data. Perhaps, this is one of the reasons why BI penetration averages only 25-35% of the workforce according to various research statistics, despite their leadership’s goal of becoming more data-driven.

The Power of a Universal Semantic Layer

When surveyed in Accenture’s Technology Vision 2023 report, 95% of global executives agreed on the necessity of new data architectures and strategies to effectively manage significant changes in their organizations’ data environments. One of the technologies named in this report is a semantic layer.

At Cube, we believe the universal semantic layer is one of those pivotal changes that companies will need to make to address today’s challenges and leverage new opportunities of the rapidly evolving modern data stack. When your modern BI platforms connect to a universal semantic layer instead of a direct database connection, you are able to support each one with a common data model, including dimensions, measures, and calculations.

To level set, a universal semantic layer is an independent yet interoperable part of the modern data stack that sits between your data sources and data consumers. The universal semantic layer allows every data endpoint, whether it is BI tools, embedded analytics, or AI agents and chatbots, to work with the same semantics and underlying data, leading to consistent and trusted insights.

Taking Modern BI to the Next Level with Cube’s Universal Semantic Layer

Without a universal semantic layer, business logic and data models are scattered between the data source and the data consumer. There is business logic in your cloud data warehouse and your transformations. Data models are embedded in the content of every modern analytics platform. When an update to the data model is required, the only option is tedious searching, copying, and pasting of platform-specific formulas and business logic. Then, add to that a little bit of hope that you updated every occurrence.

With a universal semantic layer, you can model your data once and deliver it anywhere. Regardless of the modern BI platform, people can connect to the same data and business definitions. This means that you could have Power BI and Tableau (and even spreadsheets) connect to a single data model that translates queries to the underlying data, creating a new level of consistency without the need to update embedded logic within BI platforms or files.

Integrating Cube Cloud with Modern BI Platforms

As a fully-managed service, Cube takes care of infrastructure management, deployment, and scaling of Cube Cloud, allowing your team to focus on their core data engineering responsibilities. It’s easy to start and transition your models over time without a large upfront investment, while delivering trusted data to your organization with the universal semantic layer.

Begin by taking inventory of data connections within your modern analytics platforms and cloud data warehouse. Then, identify the most popular or slowest content and queries, depending on capabilities you want to test.

Once you’ve identified the appropriate tables, build the data model by making a connection to your cloud data warehouse. Cube Cloud can read your database schema, including primary and foreign keys, to generate cube files in YAML or JavaScript. Cubes serve as the building blocks for views that will be exposed to the modern BI platforms.

Using the data model defined, you can now connect to Cube Cloud from your BI platform using the SQL API. The SQL API enables Cube to deliver trusted data over the Postgres-compatible protocol. With each interaction, Cube Cloud will translate the query from the client to the cloud data warehouse and return results. You might also add security context and query rewrite for row-level or column-level security to centralize governance policies.

In both development and production, you’ll have real-time performance insights to inspect the query lifecycle, view resource utilization, and identify new optimization opportunities. These optimizations can include improved performance and more cost-efficient cloud resource utilization. Cube Cloud can reduce cloud data usage costs from redundant queries with in-memory caching, queue management, and configurable pre-aggregation caching in Cube Store.

Finally, setup Semantic Layer Sync to push Cube Cloud data models out to the target systems. Semantic Layer Sync automatically synchronizes the data model from the universal semantic layer to Bl platforms, making Cube-specific entities, such as cubes and views, match BI-specific ones, such as datasets, tables, dimensions, and measures.

Delivering More Trust and Value from Your Data with Cube Cloud

Cube Cloud’s compatibility with popular BI platforms will ensure a seamless transition to a better way of organizing and managing data used for analytics. By adding Cube Cloud’s universal semantic layer to your modern data stack, you can truly unlock the value of your data for more people:

  • Improved Data Availability: Provide a single, unified interface for modeled data from your cloud data warehouse to every modern BI platform in your organization.
  • Consistent Business Definitions: Define metrics and dimensions once and use them consistently across all modern BI platforms and spreadsheets, reducing errors and discrepancies.
  • Governed Data Access: Streamline access to trusted data with centralized and enforced governance and security policies to promote more confident decisions.
  • Faster, More Cost-Effective Performance: Accelerate query performance through caching to ensure your analytics are as fast as they are accurate–often without querying the database directly.

For organizations looking to take their analytics programs to the next level, exploring Cube’s unified semantic layer is a step towards building context, consistency, and trust into every BI connection. By enhancing your modern BI platform with Cube, you can enable more informed decisions, faster and with greater confidence. Discover how Cube can transform your modern data stack by signing-up for a free developer account today.