The AV industry has no shortage of data, but how can it be used effectively to create better and smarter workspaces? Naila Nadeem investigates ways to make sense of data overload.
Walking through the halls of any major trade show, the same promise is splashed across countless booths: “data-driven solutions”, platforms that “optimise data”, or dashboards designed to transform the workplace experience.
In the context of the modern workplace, data is reshaping how spaces are designed, maintained, and improved. The AV industry in particular has become adept at collecting vast amounts of information. Sensors track occupancy, dashboards map air quality and heat emissions, cameras log usage patterns, and software providers claim to integrate thousands of data points into a single platform.
But this growing capability has created a parallel problem: information overload. Not all data is equal and not all of it is meaningful. In an age where information overload is very much becoming a problem, the idea of a workspace being “data-driven” can raise more questions than answers. In theory, being data-driven means making strategic decisions based on analysis and interpretation of that particular data. The definition sounds straightforward enough, and tech giants like Apple and Meta have long promoted this approach as essential for businesses to thrive. But in the corporate world, and particularly within AV where the end goal is to shape human experiences, the harder question is: what does “data-driven” action actually look like in practise?
Depending on an organisation’s goals and limitations, the answer can be very different. For some, it is about efficiency in all aspects: tracking energy use, optimising occupancy, and reducing wasted space. For others, the focus is on curating experiences by improving wellbeing or taking into account neurodiverse needs.
So, which data should the AV industry prioritise if we want to create smarter, better workspaces? More importantly, how can we convert data from abstract metrics into actions that improve the workspace?
For Yeo Yun Luo, head of technical services, AV/IT/Security at JLL, the client’s starting point when it comes to dealing with data in workspace design and management is often simple: “The first thing clients usually look at is headcount. Things like total staff numbers, and how many people are coming back to the office are very common data points that they seek. Meeting room utilisation is another key metric: how often do spaces get used? Do we have the right mix of two-person rooms, four-person rooms, larger rooms? The challenge is that data exists everywhere but there’s no single platform that pulls it all together in a way that’s truly efficient for the end user.”
Dayne Jans, director of operations, APAC at AVI-SPL identifies a more fundamental challenge when dealing with data-driven workspaces: siloed data. He notes that while plenty of data exists in various systems within the workspace, it remains inaccessible and difficult to synthesise. Jans comments: “As we all focus on data driven decisions, we’re drowning in data. The hard part is making it usable in a simple way. The real question is how to bring it all together into a single platform or dashboard to analyse it quickly and help people understand the systems within a building as a whole.”
Modelling data with intent
The gap between data collection and action, as Jans pointed out, was a problem that Evan Benway, founder of Moodsonic, saw as an opportunity. He shares: “Some end users in offices tend to track everything from CO2 levels to temperature, and while that data isn’t unimportant, are people actually using it? For us, research from Leesman has been very influential. Across global studies, the number one reason people don’t want to go into the office is sound or more specifically, environments that feel noisy even when they’re not loud. That insight tells us a lot. It shows we’re not designing with sound in mind, and that’s exactly why Moodsonic exists.”
By anchoring its platform to a specific challenge, in this case noisy offices, Moodsonic shows how modelling data with intent can cut through the noise of information overload. Benway elaborates: “Occupancy gives us one piece of the story, which is super important. At Moodsonic, we’re all about sound. We’re developing technology to make indoor environments sound better. But then part of what we found is we really need to know what’s going on in the space to get it right. That’s why we started looking at indoor conditions to inform our soundscaping. Occupancy data alone doesn’t tell us enough. You might have 20 people in an open-plan office, but are they all quiet or are they all talking? Our approach has been about using other kinds of data to solve specific problems.”
Benway continues: “The use cases we’re getting from clients are things like we now know this particular floor is occupied on certain days and empty on others. Or on Wednesday it was the sales team using the space, but today it’s engineers so the soundscape should change accordingly. Those are examples of useful data for us, helping us drive solutions and showing us how soundscaping can be data-driven.”
Marcus Rose, managing director at Valeo Technology, argues that real insights emerge when data streams are overlaid rather than tracked in isolation, citing CBRE’s Marina Bay office where Valeo Technology designed and delivered the Moodsonic soundscaping platform and sensors with its insights application.
He says: “Many companies want to avoid multiple sensors, partly because of privacy concerns. In large meeting rooms, people expect a camera but in focus pods or privacy rooms, they don’t want to feel recorded. What CBRE has done with their light sensors is simple but effective: when someone enters, the lights adjust and at the same time, the room is logged as occupied. With the Moodsonic system, that presence data is complemented by anonymised decibel levels converted to metadata, not recordings, so they can see how the room is actually being used.”
Rose illustrates further: “For example, if a focus room averages 50 dB, that suggests conversations rather than quiet solo work. Over time, CBRE can overlay these readings to spot trends: do we need more focus rooms, fewer, or a different type of space? When you cross-reference data points such as light, sound, and utilisation, you unlock real insights. Data in isolation rarely tells the full story.”
Rajesh Mittal, vice president India & SAARC region, and managing director of QSC India, sees the same challenge of isolated data when clients ask which data points matter most. He underscores: “We consistently see the greatest impact from understanding how spaces are used, how systems are performing, and how people interact with technology. Utilisation and occupancy patterns help right-size room types and reduce friction; device and system health telemetry improves uptime and support efficiency; and session-level experience cues such as meeting flow, camera and microphone behaviour, and user interaction, inform adjustments that make spaces more intuitive. Together, these signals feed a continuous improvement loop that supports ROI, capacity planning, and better day-to-day experiences.”
Much of AV’s attention has been directed toward obvious, workspace-related data points, yet there is untapped value in exploring less traditional metrics. For example, tapping into scientific research, particularly in fields like neuroscience and operational psychology, could offer a wealth of data on how people actually experience spaces. In the case of Moodsonic’s use of music in its soundscaping solutions, Benway illustrates: “We know from research that music is powerful but unpredictable because it’s subjective. That’s why we use a lot of nature-based, biophilic sounds. These work better across diverse groups because people share innate preferences for them, and there’s strong data to back this up. Studies show these sounds can influence physiology and also improve cognition, performance, and focus. We’ve even worked with hospitals. At one hospital we found that patients exposed to carefully designed soundscaping recovered faster: they slept better, reported less pain, used less medication, and got back on their feet sooner after surgery.”
The problem with data
Despite data’s vast potential, the AV industry faces significant challenges, primarily due to fragmented data collection. Jans notes: “Most manufacturers operate in isolation. Their systems capture valuable data, but it’s just one piece of the puzzle. To gain meaningful insights, you need a system or software that consolidates and manages everything in one place. This requires significant, costly integration, and even then, the return on insight is not guaranteed.”
The barriers are not only technical but also financial. Yeo highlights that for many smaller firms, investing in data management platforms is difficult to justify without a clear return. He says: “For smaller firms with less capital, they need to see the value of data management applications and platforms before investing. But it’s hard to see the value when we’re tracking too much data, and not all of it is insightful or necessary when it comes to maintaining or designing a workspace. Most clients are cost-conscious, so we still rely on the traditional way of asking questions, surveying how a space is used, and designing accordingly.”
Ultimately, the biggest challenge with data lies in evaluation and analysis. The impact of technology in a workspace isn’t always reducible to neat metrics, especially when it involves harder-to-measure outcomes like productivity or wellbeing. Benway shares how Moodsonic navigates this limitation: “Moodsonic is usually deployed alongside other workplace changes, so it’s not a pure laboratory environment. In lab studies, we’ve seen clear effects on cognitive performance and physiological factors. In the real world, we often rely on a mix of surveys and quantitative data but honestly, the biggest proof point that our solutions work is expansion. A client will deploy Moodsonic in one site, and then quickly want it in another. We’ve also seen studies tracking occupancy. Over time, more people come into the office and spend longer there. That’s a strong signal that Moodsonic works.”
For Yeo, the way forward may also be to align data collection with existing frameworks that already carry credibility. In the context of Singapore where Yeo is based, these may mean frameworks such as WELL Building Standards or Singapore’s Green Mark certifications. Yeo suggests: “If we build data collection around these certifications, the metrics become meaningful. They link directly to outcomes clients care about, like achieving WELL standards or making sure their workspace is adhering to ESG requirements. Some integrators tend to sell solutions without fully understanding client operations. At the same time, consultants sometimes tend to over-specify a space with different kinds of technology. If there were baseline standards for data collection that everyone could refer to in AV, it would prevent disputes and create common ground.”
To this, Mittal champions for an outcome-driven approach to cutting through the data noise. He says: “Start with outcomes then work backward. For example, the Q-SYS full stack AV platform brings hardware, software, and services together, so signals are normalised, contextualised, and turned into real-time actions that actually matter which leads to simpler user experiences, higher reliability, and measurable efficiency. Because the platform is open and scalable, data flows where it needs to without creating silos, allowing teams to focus on what improves collaboration rather than managing tools. In short, it’s less about collecting and more about transforming the right inputs into timely, human-centred decisions.”
The way forward with data
If the biggest challenge lies in integrating the multitude of data streams, the next stage for AV must be software platforms that not only capture but also contextualise information.
For Mittal, the key is convergence. He shares how Q-SYS puts that into practice: “Q-SYS goes beyond traditional AV by connecting audio, video, control, data, and cloud management under one platform so insights can drive real-time action. For example, VisionSuite automation uses AI-powered presenter tracking, active-speaker detection, and voice localisation to turn rooms into adaptive environments so users can focus on the task, not the tech. Reflect monitoring surfaces fleet health and usage trends through the cloud, enabling proactive support, fewer truck rolls, and faster resolution. Context from schedules, utilisation, and interaction patterns drives consistent presets and behaviours across locations, scaling quality without adding complexity. And because it’s software-defined, organisations can expand across spaces or campuses and adopt new capabilities through updates rather than rip-and-replace cycles.”
The value of data does not end at the design and deployment stage, but holds through to the maintenance and upgrade stage. Mittal explains: “After go-live, the platform’s value compounds. Trends from device and room telemetry enable earlier detection and prevention of issues while usage insights guide layout, policy, and preset refinements that keep spaces intuitive. At the same time, software-defined updates deliver new features and security enhancements at scale. The result is a living system that evolves with the organisation which helps to protect initial investments, maintaining consistency across locations, and sustaining measurable outcomes over time.”
Looking ahead, Mittal argues that the industry must embrace data intelligence as a design principle rather than an afterthought. He concludes: “We’re moving from reactive monitoring to adaptive, context aware experiences that are flexible, scalable, and intelligent by design. AI, especially through VisionSuite, acts as the ‘eyes and ears’ of the room, translating real-time signals into actions that keep spaces consistent and effortless to use. At the platform level, cloud-managed analytics will continue to inform planning and budgeting, while an open, interoperable architecture ensures the system can grow across diverse ecosystems. The goal is simple: help organisations create smarter, more connected environments that are efficient, human-centric, and ready for what’s next.”