Every day, data analysts face the challenge of understanding and creating reports from their data warehouse. This difficulty may stem from the fact that data comes from multiple locations and often does not cohesively align. However, with a few tools at your disposal, you can efficiently create data modeling reports based on your organization's unique business needs. These techniques are some of the most effective data modeling techniques for data analytics teams everywhere.

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Table of Contents

  1. Understanding Data Modeling
  2. Advantages of Data Modeling
  3. Understanding the Types of Data Models
  4. Types of Data Modeling Techniques
  5. Using Xplenty for Your Data Modeling Strategy

Understanding Data Modeling

Data modeling is a data analysis technique that allows you to define a data warehouse schema to organize your data analytics. The main objective of data modeling is to help teams visualize the relationships between their data analytics to understand better how it all interconnects. Organizations that lack these skills are unable to build relationships between data points to obtain better insights. There are various ways that you can implement data modeling, and each business should have an approach specific to their needs.

Advantages of Data Modeling

Data modeling is a powerful data analysis technique that allows you to define the data warehouse schema to visualize how it interconnects with other data points. Some of the advantages of data modeling include:

  • Improved data quality.
  • Better allocation of human and computational resources.
  • Enabling foresight into business problems and trends.
  • Increase in cross-functional communication.
  • Stronger communication with stakeholders.
  • Increase in decision accuracy.
  • Streamlining business operations.

While the benefits are very apparent, it is essential to note that data modeling is a long process. Data analytics teams must have the resources and expertise for data modeling to be successful. Data models are not just limited to data warehouse environments but can also be applied with Big Data solutions such as Hadoop Ecosystems.

Understanding the Types of Data Models

There are different data models you can use to organize your data analytics. While they all have the same goal, each model is designed for a specific purpose and will have varying requirements depending on their needs.

Conceptual Data Models

Conceptual data models provide a simplified view of your organization's business processes and help you understand which entities exist within it. This is where you'll define what columns will be in each table and other essential information that helps identify relationships between tables.

Logical Data Models

Logical data models provide a more detailed view of how data is collected and stored within your business. The main objective here is to move from the general idea you had in your conceptual data model into something more manageable for coding purposes. This model gives you a better understanding of what columns will be included, what tables they're on, and any relationships between them.

Physical Data Models

Once the logical data model has been created, you can use it to make your physical data model. This model provides specific information about column types (e.g., integer, string), table names/identifiers (i.e., primary keys), and the location of each data element, etc. This level of detail helps ensure that all team members are working with the same information in their development environment.

Types of Data Modeling Techniques

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There are various data modeling techniques you can use to organize your information. Here are a few of the most common techniques that help data analysts get started with their analysis:

Hierarchical Data Modeling

Hierarchical data modeling allows you to create a hierarchy of your data elements based on parent-child relationships. This technique helps organize complex sets of related information for reporting purposes and helps identify any overlap between tables. Strengths of hierarchical data modeling include:

  • Naming conventions that help understand relationships between tables.
  • The ability to report on parent/child data elements with ease.
  • The quick identification of the total number of records for a specific entity (i.e., all dogs or cats).

Weaknesses include:

  • Difficulty in querying detailed information about child entities, such as names and birthdays. To do this accurately, you would need additional joins, which can slow down your queries tremendously depending on how many join operations there are going back and forth between different tables.

Relational Data Modeling

Relational data modeling uses the two main entities within your data model (e.g., Customers and Orders) to create one or more tables with rows and columns for each entity. This technique helps you understand how certain information is related between parent-child entities, such as common fields and foreign keys. Strengths of data modeling include the ability to:

  • Store multiple entities within one table (e.g., Customers and their associated Orders).
  • Create complex reports that include data from multiple tables. This technique is the most commonly used when designing business intelligence solutions.

Weaknesses of this model type include:

  • The need for joins to report on certain elements, such as counts or averages (i.e., SUM function), slows down your query performance depending on how many join operations you have between different tables/entities.

Entity-Relationship (ER) Data Modeling

ER data modeling is one of the most popular data modeling techniques used today. This technique uses entities, attributes, and relationships to define how your data will be structured. For example, an entity could represent a customer record while its attributes are name, age, etc., which describe that Person's information. Strengths of entity-relationship data modeling include:

  • It provides a higher level of abstraction than other models.
  • This technique is the most commonly used when designing business intelligence solutions.

Weaknesses of this model type include:

  • This model type requires a more complex understanding of how data is structured.

Object-Oriented Data Modeling

Object-oriented data modeling is ideal for complex and constantly changing data. This technique uses objects (e.g., Person) and how they relate to other types of information like Employees, Students, etc. Strengths of object-oriented data modeling include:

  • It creates a very flexible and dynamic data model.
  • It easily adapts to constantly changing business requirements, such as adding new objects or modifying existing ones. This technique helps in designing Big Data solutions that need complex analytics with many different conditions.

Weaknesses include:

  • Complexity in understanding how multiple object-oriented relationships work together for reporting purposes.

Dimensional Data Modeling

Dimensional data modeling is used for data warehousing purposes because it helps organize large sets of information into more manageable chunks. This technique uses the concept of fact tables, which contain rows and columns that group related information about a specific subject. Strengths of dimensional data modeling include:

  • The ability to easily create detailed reports on a specific data subset, such as customer-specific information.
  • Fact tables are easy to use for querying purposes.

Weaknesses include:

  • A dimensional data model is challenging to maintain since changes in one area can have a ripple effect throughout the entire structure.

Using Xplenty for Your Data Modeling Strategy

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The main objective for creating data models should be to simplify the process of understanding complex data analytics results. One of the best ways to do this is by using effective visualization strategies throughout processes like ETL(Extract Transform Load) or ELT (Extract Load Transform). Xplenty provides one of the best ETL solutions that can help streamline your data modeling techniques. Xplenty can centralize data while creating consistency and understanding. This is especially beneficial when taking data from multiple databases and centralizing the data on a Big Data platform such as Microsoft Azure. If you are interested in using the top ETL for your data modeling strategy, contact the Xplenty team for a seven-day demo today and see how they can help you optimize your business.