By Krishna Prasad, Co-founder and Chief Product Officer, nhance Digital Builtech Pvt., Ltd.
The rapid evolution of technology within buildings combined with increasingly high expectations from end users and tenants have resulted in building owners and facility operators facing mounting challenges. On one hand, there is a need to meet the dynamic needs of the present. On the other, real estate leaders must also anticipate future integrations and expansions and devise strategies in line with the same. In this context, smart buildings with end-to-end integrated and digitalised operations are key to ensuring enhanced user experiences and future-proofing investments in line with the predicted needs of the future
The efficiency and value of a smart building lies in its ability to seamlessly integrate data from various services, features and systems, and its capability to ingest, receive, share, store, process and retrieve the data for drawing meaningful insights and delivering on the smart building experience. Data modelling plays an important role in enabling this process.
Data modeling in smart buildings is a complex process, as getting different data platforms to communicate with one another and interface in real-time with the physical world can be daunting and challenging. In the smart building industry there have been a few initiatives that are helping move the bar towards a common framework, such as RealEstateCore, Brick Schema, and Digital Twins Definition Language (DTDL) from Microsoft Azure. Therefore, real estate owners and facility managers must invest in technology platforms that channel these initiatives towards key business objectives to achieve optimal outcomes.
The Need for Better Data Processing in the Built World
The built environment today has pre-installed technologies that have traditionally been a part of a default check box. These are traditional systems that are a part of the must-have, should-have decisions based on the budgets and the think tank that worked on the building.
While there have been various innovative solutions to solve the challenges of facility managers and occupants, most companies have implemented these features as silos – be it smart HVAC, adaptive lighting controls or meeting room and desk occupancy monitors. If these features are not interconnected, we cannot derive the benefits multiplier. This is where data modeling and defining relationships between data come into play.
Key Challenges in Data Modeling
Currently, data modelling for smart buildings involves various challenges such as large data volumes, varied types of data, data velocity and data quality. One of the primary challenges in data modeling for smart buildings is the sheer volume and variety of data generated by numerous interconnected devices and systems. Additionally, ensuring the seamless flow of data from sensors to control systems in real-time poses a significant challenge, as data modeling must account for the velocity of data transmission and enable timely analysis and response to dynamic conditions within the building environment.
It is imperative that all this data is normalised and is available for various cross-platforms for secure and seamless consumption.
Additionally, interoperability is another key challenge for brownfield projects. Developing standardized data models and leveraging open communication protocols become essential to enable seamless integration and exchange of data between diverse systems.
Advantages of Data Modelling
Data modeling offers several advantages for commercial real estate properties. Firstly, it creates awareness of the available data and its potential utilization. This, in turn, promotes consistency in naming conventions, semantics, and security measures, ensuring high-quality data. Additionally, data modeling enables organizations to enforce business rules, comply with regulations, and adhere to statutory and government policies regarding data.
Moreover, data modeling proves invaluable in helping commercial real estate professionals effectively manage their properties. By tracking key factors like energy usage, occupancy rates, maintenance costs, and tenant turnover, professionals can gain valuable insights. These insights empower them to identify areas where efficiency can be improved, costs can be reduced, and tenant satisfaction can be enhanced.
Overall, data modeling brings awareness, consistency, and better data quality, allowing commercial real estate professionals to make informed decisions that optimize property management, drive cost savings, and elevate tenant satisfaction.
The Ideal Data Modelling Strategy
Data modeling plays a crucial role as a key enabler in the realm of smart buildings. It is essential to recognize and adopt an open, non-vendor-locked flexible cloud platform for data integration—a middleware solution capable of seamlessly communicating with diverse sensors, actuators, and control systems. By embracing such a platform, a comprehensive view of the building can be created, facilitating smooth integration and synchronization of data.
Furthermore, subscribing to robust data security and data privacy measures is of utmost importance. A reliable data integration middleware platform should prioritize and comply with stringent data security protocols, safeguarding sensitive information within the smart building ecosystem.
In essence, by leveraging data modeling and selecting the right cloud-based integration platform, smart building stakeholders can unlock the full potential of their infrastructure, achieving seamless interoperability, comprehensive data visualization, and fortified data security.
The Future of Data Modelling
The future of data modeling presents both challenges and promising opportunities. With the exponential growth of data, the importance of data models in helping businesses derive meaningful insights becomes increasingly critical. Furthermore, data security and privacy have emerged as significant concerns, necessitating the integration of robust security measures into data model design.
In the era of “big data,” where massive amounts of real-time data are generated, data models are undergoing a transformation facilitated by Artificial Intelligence (AI) and Machine Learning (ML). These advanced technologies empower businesses with automated tasks, predictive capabilities, pattern identification, and substantial value delivery.
As businesses increasingly rely on data-driven decision-making, data model platforms must evolve to provide timely and comprehensive insights in a user-friendly manner. The ability to offer businesses on-demand access to crucial insights will be essential. As a result, the demand for data models will continue to surge as organizations strive to enhance their data-driven strategies.
Overall, the future of data modeling is shaped by the twin forces of burgeoning data volumes and the pressing need for security and privacy. Leveraging AI and ML, data models have the potential to revolutionize business operations and deliver significant value. As businesses become more reliant on data-driven decision-making, the demand for sophisticated data models will persist, driving innovation in the field.
