Data Modeling Best Practices

Data is a series of facts and information gathered by your organization's applications, processes, and external data sources.

While it could represent anything, these quantitative variables are the basis of most successful business ventures if handled and modeled successfully. The goal is to represent the data that make up the elements of a database. Additionally, you must group, organize, and format that data. This will enable you to keep track of your data with purpose-built models. This is where data modeling best practices come into play.

There are a few types of data models, conceptual, logical, and physical. Each of these have the goal of achieving functionality and ease of use that best supports your business. The different levels of the data model have a unique role in enterprise data management and data architecture process.

Developing and enhancing an enterprise data model is an iterative process that starts at a high level and is continually refined until all the details are accounted for.

What is a Data Model?

A data model is just a way to standardize and organize the data elements and information so that you can analyze it and draw conclusions. Data Models represent the actual data and make it more easily consumable. It also brings both the business and IT teams together. Data models document business processes and their relationships via visual diagrams. These diagrams communicate the same information just at different levels of granularity which has the advantage of supporting multiple audiences.

  • A data model is critical for interoperability.
  • It allows you to demonstrate data governance.
  • Data modeling associates IDs, relationships, rules, and data.
  • A data model demonstrates the logical interconnectivity between various elements of information.
  • It's an abstract model that allows you to visualize data semantics.

A data model ensures greater consistency, cohesion, and value as you can better visualize actual trends and insights. Data modeling is relatively easy to understand, but you still need to know why it's crucial that you rely on it for your company's growth.

The three types of Data Models

The conceptual data model is the first step in understanding the business and its processes. It helps document how the business operates, key business concepts, and how it comes together to support business operations. It’s a communication tool used by Data Architects to articulate information for a business audience, especially executives and key stakeholders.

The logical data model is the next level of detail after you've completed the conceptual data model. The logical data model further defines data structures, the relationships between them, and the attributes of each entity.

The database development team requires physical data models to build out databases. This level has keys, restraints, primary and foreign key relationships, and exact data types for each attribute.

Why Use Data Modeling?

A data model enables you to make decisions based on facts instead of educated guesses. However, the value goes far beyond that. With data modeling, you're easily able to store and access information that will benefit you and your organization. Here are a few key benefits to consider:

Improves performance

  • Structured data streamlines your database structure and overall operations.
  • Enterprise data models simplify use and optimize performance returning results quicker.
  • Most data warehouse challenges are a result of poor data modeling practices.

Increases communication and understanding about data and processes

  • Data modeling gives your team the analytics to understand trends better. This leads to more effective strategies and successful implementation that will drive your success.
  • Enterprise data models push the business to identify how data is created, integrated, managed, and moved throughout its lifecycle.

Saves time and money

  • Avoid additional maintenance requirements and streamline your development process.
  • Reduce development costs by catching errors and oversights early in the process. This will certainly improve workflow and performance benchmarks.

Higher data quality

  • Data models allow developers to define procedures that monitor and manage data quality, reducing the chance of errors.
  • The visual representation of requirements and business rules enables developers to anticipate data issues before becoming more significant.

Clear Scope

  • Agreement on terminology and descriptions results in databases that become easier to maintain and extend. The model provides a basis for driving decisions and reaching agreements.

Managed Risk

  • A data warehouse application with many interconnected tables is more susceptible to risk of development issues and challenges.

Data modeling is an essential piece for optimizing your data. It has the added benefit of delivering high-quality data that allows you to build trust and showcase what you're able to offer. When you've structured your data, you're working to prevent errors, ensuring excellent reliability and consistency.

What are Best Practices for Data Modeling?

You now know the benefits of a data model, but it's important to understand the best practices that’ll drive your success. How do you develop a repository that's not only easy to manage, but also flexible enough to deliver the business intelligence you need? Here are best practices to keep in mind:

  • Don't over-complicate your data model. Please keep it simple and scalable. Data structure tends to become more complex over time, but you don't need to start that way.
  • Keep your objective in mind. What results are you looking for, and how can your data modeling support your efforts toward that goal?
  • Check and double-check that your data is correctly structured, with the correct ID, attributes, and other variables.
  • Organize your data around dimensions, facts, and measures to best position yourself and your company.
  • Be prepared for change as your business grows. You'll need to revisit your data model to ensure that it still meets your needs.

A data model is essential to your growth and success. However, you've got to make sure that it's structured in a flexible yet straightforward way. You need to change and evolve as a company without losing sight of that results-oriented approach.

How Data Ideology Supports Your Data Modeling Needs

At Data Ideology, we work with you to determine the solutions that best support your company's data modeling needs. Then, we help you to put into place strategies and best practices. This will help structure your data model in ways that make the most sense for your company now and into the future.

Our focus is ensuring that your data model delivers the level of business intelligence that helps you achieve your goals. With premiere data modeling, your system is simple and effective, delivering the performance and reliability you've come to expect. Contact us here for a quick discovery session.

Written by Mike Sargo

Co-Founder & Chief Data and Analytics Officer at Data Ideology

Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.


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