I have worked as a hands-on consultant, data architect, data engineer and analytics solution architect for more than 25 years, and the problem I see more than any other is data modeling. This problem is significant because it creates difficulty throughout the solution architecture. Problems with data modeling lead to:

  • Inefficient development and long development cycles,
  • Frequent production failures, outages, and significant support, 
  • High operating costs,
  • Incorrect data, and 
  • Lousy database performance (reports and data pipelines)

A bad data modeling approach also causes problems upstream in analytics projects that do not seem related at first glance, specifically:

  • Challenges gathering requirements from the business,
  • Complexity mapping requirements to source data,
  • Confusion about project objectives among various stakeholders

You might have this problem if:

  • A summary version of the data model is not easy to explain or understand,
  • Requirements documents look like technical design documents, 
  • Data Engineering takes much longer than expected,
  • Significant data errors are getting through,
  • Reports and dashboard creation is slow and hard to understand,
  • Reports and dashboards are slow, and unable to deliver near time data,
  • You are unable to combine data from related subjects without additional engineering,
  • It is challenging to modify or extend existing analytics applications,
  • Every new request is like starting from scratch

Here are some possible causes:

  • There is no data architect, or the person filling the role does it poorly, 
  • The data architect understands data modeling for applications, but not for analytics,
  • The data architect does not understand the business or its requirements,
  • The data architect does not understand data engineering, 
  • The data architect does not understand how the data should be delivered for reporting. 

Here’s where dimensional modeling is a data modeling technique used in data warehousing and business intelligence. It organizes and structures data to facilitate easy and efficient querying and analysis. Here’s how it streamlines analytics development:

  • Simplicity: Dimensional models are designed to be intuitive for business users. They use familiar concepts like dimensions with attributes and facts with measures, which align closely with how business users perceive and analyze data. This simplicity reduces the complexity of the analytics development process.
  • Fast Performance: Dimensional models are optimized for query performance. By structuring data into dimensional tables and fact tables with numeric keys and measures, dimensional models enable fast query execution even over large datasets. This speed enables real-time or near-real-time analytics.
  • Flexibility: Dimensional models are flexible and accommodate changes in business requirements. They are designed to be adaptable to new data sources, new business metrics, or changes in analytical needs. This agility reduces the time and effort required for modifications and enhancements to the analytics solution.
  • Scalability: Dimensional modeling supports scalability, allowing you to handle increasing volumes of data without sacrificing performance. 
  • Self-Service Analytics: Dimensional models empower business users to perform self-service analytics. With a clear and intuitive data structure, users can easily explore and analyze data using popular BI tools or SQL queries without relying heavily on IT support. This self-service capability fosters a data-driven culture within organizations and accelerates decision-making processes.
  • Consistency and Reusability: Dimensional models promote consistency and reusability of data across different analytical applications. By defining standardized dimensions and measures, dimensional models ensure uniformity in data representation, which enhances data integrity and facilitates comparisons and benchmarking across various business functions.
  • Support for Complex Analysis: Despite their simplicity, dimensional models can support complex analytical requirements. Advanced techniques allow dimensional models to handle intricate analytical scenarios while still maintaining performance and usability.

Overall, dimensional modeling streamlines analytics development by providing a framework that prioritizes simplicity, performance, flexibility, and scalability, enabling organizations to derive valuable insights from their data more effectively and efficiently.

In my experience, I’ve witnessed firsthand the primary role of data modeling in determining the success or failure of analytics projects and solutions. The consequences of bad data modeling creep through most parts of the project and solution – inefficient development cycles, production failures, and soaring operating costs. 

Recognizing the signs of flawed data modeling—such as convoluted requirements documents or painfully slow development—is crucial. These issues often stem from a lack of expertise or understanding in data architecture, leading to misguided approaches and inadequate solutions.

Fortunately, dimensional modeling is easy to learn and will help. Its simplicity, performance optimization, and flexibility make it an indispensable tool in modern analytics development. Dimensional modeling can empower business users with self-service analytics capabilities, enforce consistency and reusability of data, and navigate complex analytical scenarios with ease.

Dimensional modeling not only addresses the immediate challenges posed by subpar data modeling but also lays the foundation for a robust, scalable, and data-driven future. As organizations continue to get more value from data, the significance of sound data modeling practices cannot be overstated—it’s the foundation of successful analytics applications.