Unlocking the value of augmented reality data

The word “sensor” has become inseparable from the internet of things (IoT), where sensors detect environmental conditions and communicate these signals bidirectionally as data, whether it’s an industrial machine reporting its operating condition or your home thermostat being turned on remotely. This data is a key driver of the IoT’s global economic impact, which McKinsey estimates could reach up to $11.1 trillion per year by 2025.

Before the IoT, the two key functions of a sensor — to detect environmental change and to communicate that data — were largely carried out by humans. Today, as augmented reality (AR) technology gains adoption, humans will soon be equipped with sensors through various AR devices, such as phones and headsets. This augmentation provides uncharted opportunities for organizations to use these data insights to drive operational effectiveness and differentiate their products and services for consumers.

The AR market today is similar to where the IoT market was in 2010, generating considerable buzz and proving early value from new capabilities for users. AR’s capacity to visualize, instruct, and interact can transform the way we work with data. Based on the lessons learned in the early days of the IoT, enterprises should be asking the question: What’s the best way to plan for AR device data and see its value, so we can build better products and processes from user insights?

Smart, connected reality means more user data

As we do that planning, there is much to learn from our recent, connected past. The 2007-2008 iPhone and Android market releases provided significant data about how customers engaged with their brand, and it gave engineers new insights into user requirements. This market disruption flipped the value proposition — applications could sense and measure customer experience in conjunction with delivering it, and it opened the door to subscription- and use-based services. With similar sensing capabilities emerging through the IoT for physical products, companies quickly built in connectivity, giving rise to smart, connected products (SCPs) that make up the internet of things. The data arms race and the emergence of the data economy has been disrupting technology laggards ever since.

Considering these proven market dynamics, the potential for AR-as-a-sensor being the next-generation modality for gathering rich data is profound. Products are already equipped with APIs and connectivity, and AR devices are loaded with machine sensors, from multiple cameras to GPS, Bluetooth, infrared, and accelerometers.

AR also unlocks a set of sensors that are often forgotten amid the frenzy of machine automation — the human capacity for creativity, intuition, and experience. Humans have incredible abilities to recognize and react to novel situations, assessing them more quickly and accurately than current technology systems can.

Consider what humans using AR devices could add to such interactions. There are valuable new data and behavior insights to gather from both unconnected products and SCPs. For an unconnected product, a person using AR-as-a-sensor tech might ask: How is this product used, and what are the user preferences? For an SCP, they might ask: How does usage affect performance, and how can this product adapt to usage?

Human interaction offers context to answer how both unconnected and connected products are really being used, how they perform, and how they can be adapted for their purpose by bringing human creativity, intuition, and flexibility to AR data gathering.

Assessing the business opportunities from new AR data

The new data created by AR establishes a feedback loop to answer questions about how a product is being used or where opportunities for customization exist. The value of this type of customer data has increasingly become core to business strategy in the new digital economy.

One way to understand the value of your data is to assess it using the DIKW Pyramid (see “The DIKW Model”), a hierarchy used in information management for understanding the transformation of raw data signals into value-rich knowledge and insights.

The DIKW Model

The DIKW model shows the hierarchy of raw data and value-rich wisdom from its analysis.

If this looks familiar, it’s because this is exactly the type of data flow that creates value for manufacturers and users of SCPs today. By feeding these insights back into their engineering systems, companies can optimize their product portfolio, design, and features like never before.

AR-driven data collection can be combined with IoT data streaming from SCPs, to drive additional context and generate more complete insights. For unconnected physical products or digital-only services, humans, interacting with these via AR, can act as sensors to deliver new insights about product or service use, quality, and ultimately about how to optimize user experience and value.

Early use cases for AR insights

There are a wide range of early example use cases for these kinds of data sources. Companies like Honeywell, Cannondale, Amazon, and DHL have created new opportunities for product strategy, value chain, and quality control activities by utilizing AR data from users and providing personalization based on this data. (See “Early AR Use Cases.”)

Early AR Use Cases

Companies are already using insights from AR data to create new products and services.

These early examples clue us in to how AR-as-a-sensor will make its way into the mainstream, creating new opportunities for manufacturers, in both product strategy and value chain activities.

Expert Knowledge Transfer. While many tout the benefits of delivering AR experiences to users, Honeywell is using AR to capture expertise from seasoned workers and improve knowledge transfer to new employees. By digitizing knowledge about a product that is revealed only through experience, Honeywell can understand products and their use in ways previously unavailable, without using embedded sensors.

Voice of the Product. For Cannondale, its newest high-end bikes are being shipped with accompanying AR apps that showcase bike features and guide users through common maintenance procedures. This is fundamentally changing the definition of the product from physical bikes alone to combined physical and digital experiences. Thanks to these digital AR experiences, Cannondale has the opportunity to gather and analyze anonymized data to deliver the “voice” of the product. By understanding what features and procedures are most used, Cannondale has a potential window into their products that can drive improved user experiences and competitive advantage.

Personalized Services. AR is billed as being transformative to e-commerce and retail, because it allows customers to visualize and try before you buy, unlike other available media. Amazon Echo Look is a new device allowing customers to capture and see virtual clothing on themselves before purchasing the real thing. In January 2018, Amazon patented “magic mirror” technology, which combined with the Echo Look, will pave the way for the next-generation dressing room. The data captured today through the Echo Look is being analyzed to create user preference profiles and curate suggested purchases based on tastes. It isn’t hard to imagine how, combined with the ability to augment those clothing suggestions back onto the customer, this new source of AR data will lead to a new level of personalized services and experiences.

Quality Control. DHL has long been at the forefront of AR technology and is on the advanced end of current AR programs. By reducing friction across logistics processes, AR delivers great value for DHL’s emplyees as they go about daily tasks. But this data does not end with the user. Using integrated computer vision to perform the task of bar code scanning, DHL now has a way to capture and log quality assurance data, allowing the company to understand where human behavior may affect order quality and process efficiency.

All of these companies’ early implementations give a glimpse of what is to come in AR experience delivery and how that data can create additional value for businesses.

Connecting the strategic dots

What about the impact to a broader data strategy? Taking a step back to this level, the implications are potentially significant. The value of many data initiatives hinges on the ability to connect the dots. While IoT, digital engagement, voice of the customer, and other initiatives continue to create significant opportunities to optimize products and processes, many enterprises are running these projects in siloes because of technological or organizational constraints.

As AR emerges as a new source of context-rich data, companies that connect the dots between multiple sources from smart, connected products to CRM data, digital engagement, and other sources of insight will create the greatest opportunities.

Enterprises that want to capitalize on these opportunities should create cross-functional leads or tiger teams dedicated to the desired outcome — improving the customer experience — rather than by the traditional functional or technology-oriented alignments.

In this new data-driven world, the whole is greater than the sum of the parts, and AR just might be the missing piece you need to complete your vision.