Smart buildings are increasingly managed automatically through sensors and the Internet of Things (IoT), promising enhanced energy efficiency and occupant comfort. However, excessive data can foster what’s known as “sensor fatigue,” which can overwhelm AI decision-making processes. The misconception that more data drives better AI analysis and automation overlooks the critical importance of quality over quantity. Too many sensors can produce redundant or irrelevant information, complicating systems and obscuring actionable insights.
Building owners and operators can fight back against sensor fatigue by being careful with over-instrumentation, creating clear and user-friendly dashboards, and prioritizing high-quality data that leads to actionable results. This way, smart buildings can achieve efficiency and comfort without getting overwhelmed by excessive data.
Why More Sensors Don't Mean Better AI Results
Equipping smart buildings with sensors is far more attainable these days as the costs of sensor devices have dropped significantly over the years. However, adding more sensors to collect additional data does not guarantee improved AI analysis, automation, or results for smart buildings. In fact, the outcomes are often more negative than positive. This issue is what fuels sensor fatigue, where operators and AI analysis tools are overwhelmed by information, struggling to make informed decisions. Excessive data often includes redundant or irrelevant details (such as minor temperature fluctuations) that clutter systems and obscure far more valuable insights that can be used to automate smart building processes in a meaningful way.
Prioritizing high-quality, goal-oriented data tied to objectives like optimizing HVAC systems or improving air quality enables robust AI analysis and effective automation. Collecting only essential data streamlines analytics, enhancing automation efficiency, and sharpening decision-making precision. Using a “quality over quantity” methodology allows smart building operators to provide efficiency and comfort while avoiding the inherent problems caused by overwhelming data volumes that hinder rather than enhance performance.
How to Prevent Over-Instrumentation and Cut Smart Building Costs
Over-instrumentation involves the installation of too many sensors which floods smart buildings with unnecessary data and complicates both manual and automated operations. To counter this, operators must first define precise smart building goals, such as reducing energy costs or improving indoor air quality, and deploy sensors that directly address them. Nothing more, nothing less.
For example, integrated building management systems consolidate data from HVAC, lighting, and security, reducing the need for redundant sensors while simplifying oversight. Additionally, performing regularly scheduled audits is crucial for identifying and removing devices that collect irrelevant data, such as those monitoring unoccupied areas like empty rooms or storage spaces, ensuring systems stay efficient and cost-effective.
By using only the most critical sensors, operators can cut back on unnecessary data, reduce maintenance costs, and boost system performance. Prioritizing HVAC sensors over less important lighting controls, for instance, focuses data collection on energy-saving goals, providing clear information that improves efficiency without overwhelming the system or its managers.
Simplify Smart Building Dashboards for Clearer Data and Faster Decisions
Complex and siloed dashboards packed with data overwhelm operators, burying actionable insights, limiting visibility, and slowing the decision-making process. To simplify, smart building IT teams should spend time grouping related data points and metrics into logical categories for intuitive navigation. Emphasizing key performance indicators (KPIs), like building occupancy levels or real-time energy consumption, keeps focus on what matters most, with secondary data available only in certain situations. This allows for more streamlined dashboards that empower operators to understand what matters the most. For instance, a dashboard displaying real-time energy KPIs sets the ITOps team up for quick HVAC adjustments, optimizing performance without forcing operators to sift through irrelevant details.
Key Takeaway: Fighting Sensor Fatigue for Smarter, More Efficient Buildings
Sensor fatigue, fueled by the myth that more data and more sensors equal better results, undermines the potential of smart buildings. Prioritizing high-quality data, avoiding over-instrumentation, and simplifying dashboards unlocks actionable insights that drive efficiency and comfort. To get the most out of smart building sensors, AI, and automation investments, building owners should set clear goals, regularly audit sensor networks, and design user-friendly dashboards to ensure data serves as a valuable tool as opposed to a series of systems and data sets that deliver inaccurate and confusing results.
