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It’s Go Time for the Built Environment! Why Data Models Matter

November 9, 2023 | Marc Petock, VP, Chief Marketing & Communications Officer
4 min read
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Data. It is transforming everything. It has changed how companies in every industry do business and manage performance. And it is no secret that data creates the most valuable information in the smart building environment. The challenge is to make the data available in the right format and deliver it to the right person at the right place and time within a secure environment. This is the basic requirement to create a data value chain. Yet, data can be bewildering and meaningless if we do not move past “data drowning” and get to a point where we are employing data modeling and standardization. Access to good data is one of the fundamental requirements for owners, operators, and service providers to maximize the performance and efficiency of their facilities.

However, significant differences in how data is identified and made ready for the different stakeholders continues to present a challenge. Achieving a common dataset and standard enables more accurate business decisions, reduces errors, and helps ensure the overall integrity and governance of the data.


So, what is a data model? In simplest terms, data models visually represent the data gathered from the connected systems, equipment, and devices that are organized around the requirements of the stakeholders who will utilize this data. It serves as a blueprint or representation of how data is organized, stored, and accessed within a database. The importance of a data model cannot be overstated, as it lays the foundation for efficient data management, accurate information retrieval, and effective communication between different stakeholders. At its core, a data model forms the basis for standardizing data across a wide range of input data from various devices, equipment, and systems.


Furthermore, a data model brings the important aspect of creating a semantic data approach that brings together Vocabulary (tag data with descriptive words like air, fan, or unit), Classifications (categorizes words used to define such things as equipment and locations and Relationships (explains connections between equipment, such as how an air handle feeds a variable air volume system).


Why is data modeling important? Because it standardizes the information contained within all the disparate building equipment and systems and enables the data to be made interoperable with each other. It also serves as a blueprint of how data is organized, stored, and accessed within a database or IDL (Independent Data Layer). A data model lays the foundation for efficient data management, accurate information retrieval, integrity, and effective communication between different stakeholders.


Here are some additional reasons:

    Organization: A data model defines the structure of the data in a clear and organized manner. It specifies data types, the relationship between the data elements and how they are grouped and stored. This organization simplifies the process of data entry and retrieval. It also reduces the opportunity for human error.

    Accuracy & Integrity: A well designed data model includes constraints and rules that help ensure integrity, reliability, and predictability. This means that the data stored within the database is accurate, consistent, and dependable.

    Efficiency: With a proper data model, databases can be optimized for efficient querying and access. The relationships and structure defined in a model help to minimize redundancy and provide a “framework” for generating queries that extract information quickly.

    Scalability: As data volumes grow, a well-designed data model allows for scalability. New data can be added without causing significant disruptions to the existing structure, and the database can be expanded to accommodate increasing data demands.

    Interoperability: he ability for multiple users across different job functions to look at data and quickly understand its source, structure, and what the model represents. This context and metadata are what makes modeling important.

    Governance: Dictates how information should be shared across business units and mandates data uniformity. It also ensures only the appropriate systems and users who need access to that information receive the data and understand it. By modeling data with an abstraction layer dedicated to merging, modeling, and securely sharing data, we help ensure proper data governance.

    Consistency: A data model ensures consistency in data representation across the organization. This consistency is especially important in systems where multiple users or applications interact with the same dataset.

    Communication: A data model acts as a common language for different stakeholders, including business analysts, developers, and database administrators. It helps them understand the structure and meaning of the data, fostering better communication and collaboration.

    Reduced Redundancy: By defining relationships between data entities, a data model can help reduce data redundancy. Redundant data can lead to inconsistencies and inefficiencies, and a well-designed data model minimizes this issue.

    Security: A data model can also play a role in data security. Access controls, permissions, and encryption strategies can be integrated into the data model to ensure that sensitive information is guarded from unauthorized access.

    Adaptability: When changes in business requirements or processes occur, a data model provides a foundation for making modifications to the database structure. This adaptability reduces the impact of changes on the overall system.

    Documentation: A data model serves as documentation for the database structure. This documentation becomes essential for maintaining, updating, and troubleshooting the database overtime.

In summary, a data model is not and should not be complicated. It is crucial for ensuring that data is organized, accurate, and accessible in a way that aligns with the needs of the organization. It provides a framework for efficient data management, effective communication, and reliable decision-making. Investing time and effort in designing and maintaining a robust data model can lead to improved overall data quality and performance. Data empowers companies to seek and make good fact-based decisions that drive better outcomes.

Marc Petock, VP, Chief Marketing & Communications Officer
Marc Petock is a pioneer in leading the Intelligent/Smart Buildings and M2M movements pushing the industry forward and has contributed to transforming and changing the Intelligent Buildings and M2M (now IoT) industries. As Chief Marketing and Communications Officer for Lynxspring, Marc leads corporate and product marketing, strategy, brand management, public relations and communications that support the company’s strategic and growth initiatives.

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Founded in 2002, and embracing open software and hardware platforms, Lynxspring develops, manufactures, distributes, and supports edge-to-enterprise solutions and IP technology that create smarter buildings, smarter equipment, and smarter solutions. The company's technologies, products and services provide connectivity, control, integration, interoperability, data access, aggregation and visualization enabling users to achieve operational and business outcomes. The versatility, functionality, and broad scope of the company’s product portfolio, combined with its extensive domain knowledge of the built environment, make it a powerful and economical solution for system integrators, building owners and operators, consultants, and equipment manufacturers. Lynxspring's solutions are deployed in millions of square feet of commercial and government settings in the United States and internationally.