Data Integration Architecture for Smart Buildings

In an intelligent built environment, bringing together data from different sources involves recognizing, changing, examining and using that information to enhance the value of a building’s most important asset.

By blending efficient data integration with innovative technology, stakeholders can gain a better understanding of how a building’s systems and components operate in harmony and establish a clear path toward reaching organizational objectives. A skilled master systems integrator and the ideal tech setup are crucial to realizing the full potential of your real estate investment.

Importance of Data Integration Architecture in Smart Buildings

The data integration architecture provides a way to organize, reconcile and present building data. With effective integration, data can lead to improved building performance, smoother collaboration among stakeholders, and heightened patron satisfaction.

In a smart building, the data integration architecture is comprised of several layers:

The source systems encompass external and internal programs, record systems, enterprise data archives and other current technology, which are integrated into a central, unified core system such as a building management system (BMS).

Having a seamless flow of data is crucial for integration, and this can only be achieved with a secure and dependable connection. The data integration transforms the collected building data and allows operators to see it in a holistic manner, providing a comprehensive understanding of the building systems. It also makes it cost-effective to access and archive big data.

Data normalization standardizes the information to make it useful and machine-readable. To enable this, technology that ensures clean, well-modeled and normalized data is crucial for fueling all applications like analytics. Data analytics converts the unstructured big data collected by the Internet of Things (IoT) devices into meaningful and actionable insights.

Smart buildings require robust data integration architecture that powers advanced automation strategies and allows stakeholders to make informed decisions quickly, given the abundance of data points.

Important Aspects of Data Integration Architecture

To consolidate data into a single architecture, various platform technologies are used. These must be comprehensive enough to support current system requirements while still providing sufficient scalability to accommodate future architectural development. Advanced data integration platforms are what enable a data-centric approach to building management.

However, systems that have not been properly maintained, such as controllers, software and network devices that are outdated or no longer supported by the manufacturer, pose significant cybersecurity risks. Operating systems with many offline devices is similar to operating them manually, preventing the automation benefits of smart system integration. Both the initial design and ongoing maintenance are crucial for a strong data integration architecture.

Key considerations include:

Data Warehousing

Data warehousing focuses on the processing and presentation involved in communicating data to end users. Although each data warehouse is unique, they all have standard features. Data warehouse applications support the user’s data needs as required.

Applications of data warehousing include:

Forecasting Profiling Summary reporting Trend analysis The operational systems periodically update the warehouse database, usually during non-occupancy times. As the data accumulates, it can be extracted, filtered, and loaded regularly to a dedicated data warehouse.

Artificial Intelligence

Among the applications in data integration architecture, those featuring Artificial Intelligence (AI) offer the most comprehensive view of a building’s systems. AI enables stakeholders to see data from various unrelated sources in a unified manner, providing the insights needed to understand the data gathered from a building’s different systems.

Additionally, AI makes data integration easier. It enhances the flow of data and improves data processing capabilities. With AI, patterns and trends in datasets are easier to uncover, leading to more precise statistical modeling and better insights. AI also enables the automation of data mapping and prediction.

Working with Service Providers

Managing data integration architecture often involves dealing with networks controlled by vendors. These vendors have access to sensitive information, giving them power over building systems, controls, and other areas. To safeguard against vendor overreach, building owners should upgrade or replace devices and software, consolidate networks, and work with a Master Systems Integrator (MSI). An MSI acts as a mediator between building owners and vendors and collaborates with IT teams to address integration challenges and achieve a unified solution.

Integration Solutions and Master Systems Integrators

Having the right components is crucial for a successful data integration architecture. An integration platform and Independent Data Layer (IDL), such as IOT Jetstream, helps establish interdependent connections between equipment and applications. With a platform like onPoint, which integrates seamlessly with building systems, IoT devices, and cloud-based apps, building owners can fully take advantage of their building’s data. These technologies help unlock a building’s full potential and optimize performance.

An MSI is vital to realizing the benefits of smart building technology. A good MSI understands a building owner’s goals and needs, and utilizes the appropriate technology and solutions architecture to solve business problems. With their support, building owners gain enhanced control, insights, and capabilities.

Last Updated on March 13, 2023 by Josh Mahan

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