Data-driven decision making is a crucial aspect of business strategy. With massive volumes of data and increasing users, businesses need an efficient method to turn raw data into actionable insights. Data modeling is at the core of this process, which creates a solid foundation to collect and analyze data. 

In this blog, we will explore the process of data modeling and its role in data analysis in general. We will also look at the different types of data modeling tools and best practices in Oracle Analytics Cloud (OAC).

What is data modeling?

Data modeling is the process of defining and analyzing data requirements and relationships between datasets to enhance business processes. It creates a visual representation of data structures used in a database management system. This includes types and relationships, how data is stored in the system, and how it’s organized and used. 

A comprehensive data model helps build a logical, robust database that eliminates errors and redundancy. It also ensures effective documentation of various data requirements in terms of usage, security, and governance.

Generally, there are three types of data models:

1. Conceptual model
2. Physical model
3. Logical model

An interesting analogy to illustrate the data modeling process is the construction of a house (database). 

The process begins with discussing building requirements like number of rooms, exterior design, etc. ((business requirements). Based on this information, an architect (data modeler) will prepare a blueprint (data model). This blueprint will help visualize the design and structure of the house after completion. Finally, engineers (database architects & developers) will construct the house (database).

Why data modeling matters?

As discussed above, data modeling is the core functional block of creating database and optimizing analytics performance. Everyone involved in data analysis should be aware of the basic concepts of data modeling. This includes database architects, data analysts, and business analysts. 

Some of the use cases include:

Impact on data warehousing

Data warehouse is a central system of data from multiple sources with similar/related data in different formats. It is essential to map out the warehouse formats and structures first. Further, it ensures these requirements are considered and used while creating datasets and enables data analysis and mining.

Enabling Big data, AI/ML, and other technologies

Data modeling is a crucial component in the early stage of system design. It acts as the foundation upon which the rest of the stages depend to establish various business rules, programs, and tools.

Through data modeling, these systems can communicate in a common language. Businesses can derive the full potential of modern technologies such as AI/ML, IoT, and edge computing.

Collaboration between business stakeholders

Using data modeling, IT teams can easily collaborate, communicate with non-technical personnel, and create data structures for analysis. It ensures that all stakeholders have a holistic view of how the business operates and how data is used.

Improve query performance

Querying is the process of retrieving information from a database. While this is a straightforward process, it becomes a challenge when working with large datasets. Vast volumes of datasets can cause the overloading of servers. Ensuring optimal query performance is crucial.

Data modeling helps to avoid repeating data. It also enables analysts to extract only the relevant data, significantly improving the query performance.

Oracle Analytics

Oracle Analytics Cloud (OAC) is a comprehensive cloud-based Business Intelligence (BI) and analytics platform from Oracle. It helps manage the entire analytics process, including data ingestion and modeling, data preparation, visualization, and collaboration. The platform also ensures that proper security and governance measures are in place. 

Oracle Analytics is available for all types of data–from the cloud, on-premises, and hybrid deployment. It enables various business users, data scientists, and engineers to collaborate and make data-driven decisions. 

OAC empowers users with numerous data modeling capabilities to ensure a single consistent view of business data. This includes:

1. Governed semantic model
2. Self-service modeling
3. Data augmentation and recommendations

Oracle Analytics data modeling tools

Oracle Analytics offers various data modeling tools to create enterprise models and self-service datasets.

1. Data Modeler- A browser-based, data modeling tool that helps developers create simple semantic models and datasets. The modeler displays models as subject areas that can be visualized and used in dashboards and analyses. 
2. Semantic Modeler- A browser-based data modeling tool to create, build and deploy semantic models to a .rpd file. 
3. Model Administration Tool-A mature modeling tool that offers comprehensive governed data modeling capabilities. It helps create semantic models from any data source.
4. Data Model Editor- A data modeling and preparation tool that enables users to create datasets having multiple tables with joins.

Best practices for data modeling

Following best practices ensures you can reap the maximum benefits and improve performance. Here are a few best practices and tips:

Documentation

Documenting the model is an excellent practice to help other stakeholders involved in the process to have greater visibility. It enables them to understand the relationships between datasets and maintain the structure over time.

Appropriate data types

Using suitable data types to store data can save a lot of time during data cleaning. You can improve efficiency by minimizing the time taken to resolve basic issues in assigning data types. For instance, storing an integer as a string can result in extra time dedicated to data cleaning.

Validation

Another best practice to follow is validating actions at every stage of process. By doing so, you can minimize the errors that might occur and ensure improved performance at every step.

Reuse

This can be done by creating data models in datasets instead of creating them in the projects. Through this method, you can save time and also utilize the modeled datasets across multiple projects. 

Conclusion 

Data modeling is a crucial part of data analysis. It enables users to reap multiple benefits, including improved collaboration, reduced costs and risks, faster time to value, and enhanced performance. There are several data modeling techniques such as conceptual data modeling, logical data modeling, and physical data modeling.

Oracle Analytics Cloud (OAC) is a cloud-based platform that provides data modeling capabilities for data analysis. It helps analysts develop a functional and secure database that can be visualized to analyze business processes and make informed decisions. 

Using a tool like BI Connector, you can connect Oracle Analytics data to modern visualization tools such as PowerBI and Tableau. With interactive dashboards and easy drag-to-drop features, you can derive actionable insights.

We have introduced a new feature that empowers analysts to connect data from Oracle Fusion Analytics apps data to PowerBI. This includes SCM (Supply Chain Management), HCM (Human Capital Management), ERP (Enterprise Resource Planning), and CX (Customer Experience) Analytics. Learn more about it here.