XR Delivering Data in Context
Anyone working in the IT Industry today is aware of the importance of Data; good Data that is. Everything we do generates data, and every piece of data has value and its value is driven by its quality, its use cases and the and the importance of the decisions that are driven by it.
“Data is the new oil”
The quote originated from an article in The Economist back in 2017. The original statement reads, “The world’s most valuable resource is no longer oil, but data”. This analogy highlights the immense value of data in today’s technology-driven world. Just like oil, data can be a powerful resource when refined and used effectively.
Data Architects around the globe have dedicated countless hours to developing Data Ontology. It is a structured representation of knowledge within a particular domain, outlining concepts, relationships, and properties to facilitate smooth data integration, sharing, and reuse among various systems and datasets. Put simply, it's a method for connecting data in different formats through specific concepts. By adding an ontology layer to define a concept, you can then link relevant data points to this concept automatically. Thus, data ontology streamlines the organisation and processing of data, serving as a bridge between philosophical concepts and practical applications. The development of ontologies adds significant value by generating metadata (more data), making each piece of data associated with an ontology not only more valuable but also more complex and creates more data!
Data/Information Overload!
“It’s not information overload. It’s filter failure.”
Clay Shirky (born 1964) is an American writer, consultant and teacher on the social and economic effects of Internet technologies and journalism
Most organizations do not struggle significantly with a lack of data; rather, they may grapple with a lack of quality data, which is a distinct issue. Data is typically plentiful. The challenge lies in accessing the necessary data, in the correct format, to facilitate informed decision-making at the opportune moment. Data should be contextualised according to the individual's identity, location, assigned tasks, or the decisions they must make. Moreover, the valuable efforts of data modellers and architects in data ontology would be further enhanced if delivered in context. This approach ensures that decisions are informed not just by prominent data elements but also by those associated within the same domain or related to the primary data, thus enriching the decision-making process. The filter referred to by Clay Shirky, in my opinion is Context.
Many say that a Digital Twin has all the data we need!
Data is the foundation on which a Digital Twin is built. A Digital Twin behaves as closely as possible to its real-world counterpart, updated using data collected from the physical twin. By simulating and predicting behaviour in real-time, Digital Twins offer insights into how objects work, allowing opportunities for drastically improving production operations.
Many companies and organisations now have Digital Twins, some of them very mature, and they are a source, or a portal to some very valuable data. They can be used to monitor operations, but are often challenged in allowing the root causes of problems to accelerate their resolution.
A Spatial Twin, also known as a “digital twin of a place”, is a type of Digital Twin. Spatial Twins offer unique insights that other Digital Twins may miss. For instance, consider a chemical plant facing problems with a compressor. The compressor frequently malfunctions. While the compressors Digital Twin identifies the error, it cannot deduce the root cause. With no SME at the site and local staff don’t know what to do, a Spatial Twin could provide the context needed to find the evasive root case.
A Spatial Twin, will allow any employee to augment what they see with new information. In this case, the fact that there minor issues with a valve close to the compressor, this is data that can be delivered by the Spatial Twin, because it delivers data in context; It facilitates Situational Awareness. This valve was showing an issue in the Digital Twin, but a minor one, and the Digital Twin did not see the relationship, but the Spatial Twin knows it is there, and knows when the valve data is abnormal, and it delivers this to the user in context, in their face, with XR and Industrial Metaverse platforms.
Spatial Twin added value
So, while the Digital Twin did provide an accurate and up-to-date readout of compressor performance, only the Spatial Twin could provide the needed context of the Compressor, and the valve in context of location to facilitate the appropriate solution.
So, while the Digital Twin did provide an accurate and up-to-date readout of compressor performance, only the Spatial Twin could provide the needed context of the Compressor, and the valve in context of location to facilitate the appropriate solution.
Spatial Twins accessed using XR & Metaverse Platform can bring a human and physical world perspective to a Digital Twin which is a wholly Virtual Environment delivering contextualised data that helps solves problems more efficiently, and often more safely. While a digital twin is a virtual representation of its physical counterpart, a spatial digital twin adds a holistic, dimensionally accurate and location-based representation to the model.
Once more, XR and Metaverse technologies are integral components of a platform that provides Spatial Twin capabilities, supported by a Digital Twin. This combination empowers individuals with Situational Awareness functions, leading to effective decision-making that resolves issues with greater efficiency.
I highly recommend reading the Spatial Twins vs. Digital Twins article on PTC’s website written by Colin McMahon, PTC are a software pioneer in this space.
Organisations who do not have or need Digital Twins, never mind Spatial Twins still have similar challenges to address. How can data be delivered to the right people, at the right time with the context needed to support the decision making process?
The Decision Making Engine
Decision-making is the engine that propels enterprises forward, ensuring they navigate challenges, seize opportunities, and achieve sustainable success.
Data is the fuel of the Decision Making Engine. We live in a world with an abundance of data and without a Twin, finding the right data to support a decision is a challenge. In my opinion, this problem has the same root causes as previously discussed, the lack of context. In such cases, the challenge is even greater. In a world with a Digital Twin, you at least know the data you need is probably in the twin somewhere, the challenge is finding it within a single source; without a Twin, the data you need could be anywhere, and could be in sources that you don’t know even exist!
Generative AI can significantly improve decision-making processes. It assists in various ways:
Data-Driven Insights: It produces synthetic data and uncovers intricate patterns, supporting data-informed decisions.
Meeting Summaries: AI tools such as Fireflies.ai, Gong.io, and Chorus.ai transcribe and condense meeting recordings, securing decisions and action points.
Content Creation: It can generate novel content (text, images, audio) from existing data, transforming data analysis and strategic decision-making.
Bias Reduction: Decisions made by AI can be more objective and inclusive, resulting in improved outcomes.
New Decision Making skills
In the near future, mastering AI interactions and identifying relevant and accurate data sources will become critical skills for decision-makers.
Moreover, Large Language Models (LLMs) such as OpenAI's ChatGPT and Microsoft's Copilot have demonstrated potential in aiding decision-making, though they encounter certain challenges. LLMs can assist in the following ways:
Instant Advice: By analysing data from diverse sources, LLMs can offer rapid advice and suggestions. They take into account various factors and constraints to provide tailored recommendations.
Data Analysis: Specialising in processing textual data, LLMs are instrumental in deriving insights from unstructured data. They are adept at summarising market research, evaluating customer feedback sentiment, and compiling financial reports.
Ethical Considerations: The integration of LLMs into decision support systems necessitates a thorough examination of ethical, technical, and human aspects. It's essential to recognise the limitations of LLMs even as they contribute to increased efficiency.
The potential of LLMs to enhance decision-making processes is significant, yet it is imperative to ensure their responsible deployment.
In summary, Generative AI and LLMs can aid in gathering the necessary data for decision-making, while XR Technologies can support this process by:
Offering an effective user experience in accessing General AI and LLM content.
Driving the input to General AI and LLMs with the additional aspect of context.
Facilitating easy navigation and combination of data and content sources on the infinite canvas provided by XR technologies, eliminating the barriers imposed by a limited set of screens or tabs within a browser window. Comparative analysis is much easier with an infinite canvas.
Providing efficient methods to visualise data and content, and to share it with others for collaborative decision-making.
Delivering contextual data is not only valuable to decision making, it also has great benefits to guided work instruction and the execution of many types of workflows executed by front line workers and other resources within an organisation.
Augmented Data anchored to Physical Objects
Delivering contextual data from enterprise applications and IoT sensors can aid in the correct execution of maintenance and standard operating procedures.
*Image provided by PTC
Software solutions like those provided by PTC with their Thingworx and Vuforia platforms an help organisations build XR Experiences that guide resources to execute every day procedures by the provision of step by step instruction, and by providing the contextualised data needed to execute the procedure.
Simple Guided Work Instruction
Providing step-by step guidance to support the execution of maintenance procedures.
Microsoft also provide solutions to build simple procedures with step-by-step instructions with their Dynamics 365 Remote Assist and Guides applications to support Field Workers.
In addition there are many other software solution vendors that provide similar functionality and the right choice is dependent on many factors, however these tools should be part of a structured ecosystem of components built on a solid architectural framework that integrates well into an organisations infrastructure, and aligned with their XR and Metaverse Strategy, this will be described in more detail in a future blog post.
Whenever I talk about XR to various groups of people my main pitch is around XR provides Context. However one of our leaders (thanks Karina!) came up with a great statement that encapsulates a key objective of XR and Metaverse technologies:
“We know how to create data,
We know how to analyse data,
We are learning how to experience data”
By delivering data with context, XR enables the visualisation of data in a way that will support and accelerate decision making, as well as presenting it anchored to physical world objects that brings benefit to many business processes and workflows.
XR and Metaverse Technologies can transform the way we work by allowing us to experience data.
Please note that the opinions specified here are my own and may not reflect the views of my employer.