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How to Make Your Control Systems Produce More Universally Usable Data

Image of Patrick Gilhooly
Patrick Gilhooly

In a world where buildings need to adapt more than ever, the ability to harness data from control systems with limited capital outlay is a real game changer for digital transformation efforts, meeting sustainability goals and continuing to deliver comfortable spaces to occupants.

While a building may have a building management system that seems capable of data extraction to the cloud, the journey from raw data to actionable insights often hits a roadblock at the device and network layer. At Buildings IOT, we believe that open and well-structured control systems are the cornerstone of analytics-friendly environments. This article will explore how to set up your control systems to produce universally usable data, paving the way for seamless analytics and improved operational efficiency.

The Foundation: Device and Network Layer Setup

The first step in ensuring that your controls systems produce usable data is to focus on the setup of the device and network layer. This involves configuring devices and networks to avoid generating what we colloquially refer to as "gobbly-goop"—incomprehensible or poorly formatted data that can hinder analytics.

  • Standardization is Key: Ensure that all devices conform to standardized communication protocols like BACnet. Standardization makes data more accessible and interpretable across various platforms and applications. With standardized data, you can avoid the need for complex translation processes, thus reducing errors and improving data integrity.
  • Naming Conventions Matter: Implement consistent and meaningful naming conventions for devices and data points. This makes it easier to identify and manage data streams, facilitating quicker integration into analytics platforms. For example, instead of using vague identifiers, use descriptive names that indicate the function or location of the device.
  • Structured Data Formats: Adopt structured data formats that are widely recognized and compatible with most analytics tools. This includes using formats like JSON or XML for data transmission and storage. Structured data is easier to parse and analyze, allowing for more efficient data processing.

Ensuring Data Availability and Accessibility

For data to be truly usable, it must be readily available and easily accessible. This involves creating a network environment that supports open and secure data exchange.

  • Open Access Controls: Implement access controls that allow for secure yet open data exchange. This means setting up permissions that enable authorized users to access and interact with data without cumbersome barriers. Open access fosters collaboration and innovation, driving more meaningful insights from the data.
  • Interoperability Across Systems: Ensure your control systems are interoperable with other systems and platforms. This can be achieved through the use of open protocols and APIs (Application Programming Interfaces). Interoperability allows for seamless data flow across different systems, enhancing the overall capability to analyze and act on the data.
  • Scalable Network Infrastructure: Invest in a network infrastructure that can scale with your data needs. As the volume of data grows, your network must be able to handle increased traffic and storage requirements. A robust network infrastructure supports continuous data flow and prevents bottlenecks that can disrupt analytics processes.

From Data to Insights: The Role of Analytics

While the primary focus is on making data usable, it's essential to understand the role of analytics in this process. Analytics transforms raw data into actionable insights, but this can only happen if the data is in a format that analytics tools can readily interpret.

  • Data Normalization: Normalize data across different sources to ensure consistency. This involves converting data into a common format or structure, making it easier to compare and analyze. Data normalization is crucial for accurate analytics, as it ensures that all data points are comparable and meaningful.
  • Advanced Data Processing: Utilize advanced data processing techniques to clean and prepare data for analysis. This includes filtering out noise, correcting errors, and filling in missing values. Proper data processing ensures that the insights derived from analytics are reliable and actionable.
  • Visualization and Reporting: Leverage data visualization tools to present data in a comprehensible and actionable manner. Effective visualization helps stakeholders understand complex data trends and make informed decisions. Reporting tools can automate the generation of insights, making it easier to share and act on the data.


Creating universally usable data from control systems is fundamental to harnessing the full potential of analytics in adaptive buildings. By focusing on the setup of the device and network layer, ensuring data availability and accessibility, and understanding the role of analytics, you can transform your control systems into powerful tools for operational excellence.

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