By Sumanth Reddy Dubbudu, HEXstream data analyst

Every effective analytics solution in Oracle Analytics Cloud (OAC) relies heavily on data modelling. Data format has a direct impact on report performance, usability and scalability in real-world applications. Datasets and semantic models are the two main methods that OAC offers for getting data ready for analysis.

Both are intended to facilitate reporting and visualization, but their purposes, designs and levels of complexity are different. Building effective and maintainable analytics systems requires selecting the appropriate strategy.

Here we will discuss what datasets and semantic models are, how they differ, their benefits and drawbacks, and when to apply each in real-time applications in this blog.

What is a dataset in OAC?

In OAC, a dataset is a self-service layer for data preparation that enables users to rapidly upload, merge and modify data for analysis. It is primarily intended for analysts and business users who require rapid insights but lack in-depth technical understanding.

The Data Visualization (DV) interface is used to construct datasets, which support a variety of data sources, including database connections, Excel files, and CSV files. Within OAC, users can carry out tasks including joins, filters and data enrichment.

Datasets are perfect for quick dashboard development and ad hoc analysis, where flexibility and speed are more crucial than intricate modelling.

What is a semantic model in OAC?

In OAC, a semantic model is an enterprise-level data model that is used to provide security rules, hierarchies, calculations and structured relationships. Usually produced by data engineers or BI developers, it is comparable to the RPD in OBIEE.

By offering a consolidated layer of business logic, semantic models guarantee uniform definitions in all dashboards and reports. Advanced features including reusable computations, aggregation rules, and row-level security are supported.

Large-scale, enterprise-reporting systems where data integrity, governance and performance are crucial are the ideal candidates for this strategy.

Standard usage in OAC

In real-time OAC implementations, both datasets and semantic models are often used together.

·       Datasets are used for quick exploration and self-service reporting.

·       Semantic models are used for enterprise dashboards and governed reporting.

This combination enables organizations to balance flexibility and control.

Understanding data flow in OAC: dataset vs. semantic model

The below diagram shows how data flows in Oracle Analytics Cloud (OAC) from multiple sources to final dashboards using two different approaches: datasets and semantic models. 

At the top, data originates from various sources such as databases, Excel files, and APIs. From there it splits into two paths. The dataset path represents a self-service approach where business users prepare data using the data prep interface by performing tasks like joins, cleaning and transformation. 

On the other hand, the semantic model path represents a governed and structured approach. Here, data is modelled using a dedicated data modelling tool where developers define relationships, measures, and hierarchies. This ensures consistency, reusability and strong data governance.

Both paths ultimately lead to visualizations and dashboards, showing that regardless of the approach, the goal is to deliver insights. The key difference lies in the level of control, complexity and scalability each approach offers.

Advantages of dataset in OAC

·      Simple to construct: Without technical knowledge, businesspeople may produce datasets.

·      Quicker development: Makes it possible to create and analyse dashboards more quickly.

·      Adaptable: Facilitates rapid conversations and a variety of data sources.

Disadvantages of dataset in OAC

·      Limited scalability: Unsuitable for systems used in large enterprise.

·      Duplication of data: Inconsistent data may result from many datasets.

·      Limited security: There is insufficient support for advanced security measures.

Advantages of semantic model in OAC

·      Centralized business logic: Guarantees uniformity in every report.

·      High performance: Designed to handle big datasets and intricate queries.

·      Advanced security: Role-based and row-level security are supported.

·      Reusability: A single model can be applied to several dashboards.

Disadvantages of semantic model in OAC

·       Higher complexity: Requires strong technical knowledge to design.

·      Longer development time: Not suitable for quick analysis needs.

·      Dependency on developers: Business users cannot easily modify it.

Real-time project scenario

In a real-time OAC project, a marketing team used datasets to quickly analyze campaign performance by uploading Excel data and combining it with database tables. At the same time, the finance team relied on a semantic model for enterprise reporting, ensuring consistent revenue calculations and secure access across departments.

This approach allowed the organisation to maintain both flexibility for business users and governance for enterprise reporting.

Common mistakes to avoid

·      Using datasets for large enterprise reporting.

·      Ignoring semantic models for security-critical data.

·      Creating multiple duplicate datasets.

·      Mixing business logic across different layers.

Conclusion

Both datasets and semantic models play important roles in Oracle Analytics Cloud. Datasets provide flexibility and speed for self-service users, while semantic models offer structure, governance and performance for enterprise-level analytics. Choosing the right approach depends on the complexity of the data, the scale of the solution, and the needs of the users.

A well-balanced strategy that leverages both appropriately leads to a more efficient, scalable and reliable analytics environment.

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