Augmenting the Enterprise Data Warehouse: The Pros and Cons of OLAP Cubes

In modern analytics, a data warehouse serves as the central hub for storing and managing historical business data, while Online Analytical Processing (OLAP) enables organizations to turn that data into actionable insights. OLAP systems sit on top of a data warehouse, organizing large volumes of information into multidimensional structures—often called OLAP cubes—that make it easy to slice, dice, and analyze data from multiple perspectives.

Together, the data warehouse and OLAP form the backbone of enterprise business intelligence. The warehouse stores and integrates data, while OLAP provides the analytical layer that transforms it into reports, dashboards, and visualizations that power smarter decisions.

As data grows in scale and complexity, traditional OLAP models are evolving. Enterprises now face a new challenge: balancing the stability and governance of legacy data warehouses with the flexibility and speed required by modern cloud and third-party data sources.

How OLAP works within the data warehouse

A data warehouse and OLAP system work together to make large-scale analysis possible. Here’s how the process typically flows:

• Data source: Transactional systems (OLTP) collect operational data such as sales, orders, and inventory.
• ETL process: Data is extracted, transformed, and loaded (ETL) into the warehouse for consistency and accuracy.
• Data warehouse: Stores integrated, historical, and structured data optimized for analysis.
• OLAP cubes: Organize this data into multidimensional models that allow users to view performance by time, geography, product, and more.
• OLAP tools: Enable analysts and business leaders to perform complex queries, run “what-if” analyses, and visualize results across departments.

This combination ensures that enterprise teams can analyze historical trends without disrupting operational systems.

OLAP vs. OLTP: The difference in purpose

OLAP and OLTP are both critical to enterprise data management, but they serve very different functions.

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In short, OLTP systems run the business, while OLAP systems help you analyze the business.

The risks of this are clear

It is not just that this potentially business-critical dark data is outside of the management scope of IT, but also that to work with dark data, business users need to take data out of the warehouse and into offline systems to bring the sources together—which loses the certification and accuracy of that data. For instance, spreadsheets are constantly exported for custom reporting and insights.

IT leaders are left with two challenges to solve: how to bring this dark data into the light so it is available for decision making, and how to do this in a way that complements existing warehouse architecture and policies. The answer is an augmented data warehouse using a system like Domo, which can run two architectures in parallel and knit them together through common interfaces.

The challenge: Balancing stability, security, and agility

IT leaders today face two conflicting demands when managing enterprise data architecture. On one hand, there’s the need for stability, security, and compliance for business-critical data stored in the data warehouse. On the other, there’s growing pressure to support new and constantly changing data sources—from cloud applications, APIs, and third-party systems that don’t fit neatly into traditional warehouse models.

As more business processes move online, external data sources are becoming vital to marketing, sales, HR, and operations. However, these sources are often unstructured, high-volume, and dynamic, which makes them difficult to integrate into rigid warehouse schemas.

When IT can’t easily bring these feeds into the warehouse, they often become “dark data”—information handled outside approved systems in spreadsheets or local files. This creates silos and risks:

• Critical data exists outside IT’s visibility and governance.
• Business users export certified data into offline tools, weakening accuracy.
• Departments build their own custom datasets, introducing conflicting metrics.

These challenges leave IT leaders with a pressing question:

‍How can we make all this data available for decision-making—securely and at scale—without compromising the integrity of our existing warehouse?

The answer lies in an augmented data warehouse approach, where systems like Domo complement the traditional warehouse by managing both governed and flexible data environments in parallel.

Augmenting the data warehouse

Data warehouses that pipe data into OLAP cubes can provide analysis of well-structured data by pre-calculating common views and dimensions of data. This can free up bandwidth for business intelligence analytics on common tasks. Automation of these dimensions also reduces errors and makes management and tracking on hundreds or thousands of data sources feasible.

However, there are areas where cubes aren’t as beneficial, for example with third-party data that needs to be flexible (if it’s occasional or ad-hoc), or with unknown applications that are suited to exploration and agile problem-solving (for instance, if an organization needs to collect large volumes which may not be needed often, and it is not realistic to engineer this data into the cube / enterprise data warehouse format).

The resolution to this problem is to augment the existing data warehouse with a solution like Domo, which covers the two major needs: the flexibility of handling data which isn’t suitable for the enterprise data warehouse, but with all the controls (row level access, usage reporting, and so on) that the enterprise data warehouse provides to IT leadership for its data.

While traditional OLAP cubes remain foundational to many enterprise data warehouses, they’re often being reimagined within modern cloud architectures. Cloud OLAP platforms can now integrate directly with data warehouses to deliver the same multidimensional analysis but at greater speed, scale, and flexibility.

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How augmented OLAP works in practice

In new architectures, Domo can replace OLAP cubes altogether while maintaining the same level or better data governance, security, and data lineage so IT can sleep better at night. Alternatively, it can also run alongside an existing data warehouse and focus on different data sources that aren’t well suited to the existing systems.

Domo can then integrate with existing data management and cataloguing, with users able to discover this data in a universal way, through consistent tooling. From a security and compliance perspective, Domo provides row level visibility of data it handles and provides familiar reporting on usage, security and user / group access to datasets, so certification and compliance can be achieved.

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When it comes to accessing these data sources together, enterprises can bring data into their existing BI tool of choice. For customers already using a visualization tool from another vendor, Domo can use an ODBC driver to connect their SQL server, and can also use its write-back connectors for bi-directional semantic transformations to occur across databases. Domo provides the backend connected architecture, which can also be further configured via a Java command line interface, or analogs that engineers can use for deeper customization into more restrictive business logic and processes.

Modern OLAP in the cloud

Traditional OLAP cubes were originally designed for on-premises data warehouses—rigid, schema-heavy environments that served structured, historical data. Today, that model has evolved.

Cloud-based OLAP platforms can now process massive data volumes in real time, integrating directly with cloud warehouses such as Snowflake, Amazon Redshift, and Google BigQuery. These systems achieve the same multidimensional analysis as classic cubes but with far greater speed, elasticity, and scalability.

Modern OLAP also allows dynamic modeling, meaning analysts can explore data interactively without rebuilding cubes or waiting for batch processes. This approach reduces IT bottlenecks and empowers business users to discover insights faster.

The result is a more unified and agile data ecosystem—one that blends the governance and trust of traditional data warehouses with the flexibility and performance of the modern cloud.

Summary

By solving the dark data problem in a way that works alongside an existing data warehouse, both IT and business users can achieve their goals without compromising.

OLAP remains a key component of modern data warehouse strategies, providing the analytical power behind dashboards, forecasts, and strategic insights. With Domo, organizations can extend these OLAP capabilities into the cloud, combining governed enterprise data with flexible integrations and real-time collaboration. The result is faster analysis, stronger data governance, and a more complete view of the business.

Find out more about Domo’s products or contact us to talk about your current and future needs.