Einstein, Psychology and Data Fabrics to Power the Metaverse
Einstein made a great comment: “No problem can be solved from the same consciousness that created it.” Or in other words, the thinking that you use in order to identify a problem is probably not going to be the same thinking that you're going to use in order to solve that problem. The solution space will inevitably go beyond your current thinking today to reimagine, rethink, re-architect, and find patterns where they don’t exist today.
Where does industrial digital transformation stand today? Have companies actually seen the real value from their transformation initiatives? And you can argue yes, and you can argue no. There's many inhibiting features as to why companies haven't reached their full goals.
Let’s put a pause on this conversation for just a moment and discuss where we're going and what we're trying to achieve in collective society with this interplay of technology. And in terms of the Social Metaverse, Mark Zuckerberg has done a good job at putting that pacemaker ahead of us in terms of guiding vision. We're heading towards the Social Metaverse, we're heading towards the Industrial Metaverse. But what does that really mean? And how are we actually going to get there?
What are the core building blocks that will enable the Metaverse?
And so let’s go back to this conversation about why have we stumbled in terms of collective digital transformation? Why haven't we used data in all the right ways? Why haven't we digitized certain industries? Why are we so slow? What’s really going on?
It comes to issues of collective psychological barriers of thought, but it also comes to issues of technical prerequisites. Let’s first examine the psychologies because eventually the psychologies and the technical prerequisites are going to come to a crossroads. Our motivations around the use of technologies and the nature of the tools themselves will come to a crossroads at the most foundational architectural levels.
Psychological barriers in the way that we think will ultimately point to infrastructural and architectural barriers.
The Value of Data
The value of data is something that everyone is trying to maximize.
How do we maximize our bottom line?
How do we maximize profits?
How do we make more from this goldmine of information?
And presumably, the thought is that by using analytics, by using AI, by using ML, we can dissect data for patterns that we can't conventionally see in an Excel spreadsheet. Patterns that we can't plot on a simple X and Y axis. That somehow this multivariable way of thinking will give us deeper insights into the way people act and human behavior. Why would people click on a certain tile in Netflix based on the composition of an image, or why would someone buy a certain product on Amazon versus another one? Deep insights from data.
We think about data as a pipeline, and how we’re going to transform that data into something else in order to extract some net benefit out of that data. And this very thought process of viewing data as a pipeline is one of the psychologies that needs to be broken. Data is not a pipeline. Data should not be viewed in the context of a transformation pipeline.
And the view of data as a pipeline is also analogously reflected in terms of technology stacks. Fundamentally, data is a network. Data needs to be viewed from the perspective of platforms and ecosystems, and data needs to be viewed in terms of the interaction of different datasets.
Data is a network, data is not a pipeline. This is the first psychological barrier that needs to be disrupted. And the first psychological barrier immediately points to the second barrier. If the first psychological barrier is characterized by the value of data itself, the second pyschological barrier is characterized by the value of what we do with data and the value of analytics. Both barriers can be broken by thinking about data as a network.
“Fundamentally, data is a network.”
The Value of Analytics
The best and most advanced algorithms today whether it be AlphaGo, AlphaFold, WaveNet, Tesla Autopilot, LaMDA AI, Siri, ChatGPT, etc, are judged in comparative performance to human cognition and behavior. The relative success of an algorithm is always in comparison to whether it can beat a human in a similar task. Can the algorithm mimic human capabilities, does it have superior capabilities in finding patterns, can it perform tasks with greater accuracy, so on and so forth.
As an aside about AI capabilities, there was an interesting article recently in terms of Google's chatbot LaMDA. A Google Developer associated with the project put out a public warning that he believed the Google chatbot was actually sentient, that it had some form of consciousness, personality, free-thinking, or capability to do such. The term “artificial intelligence” is really living up to its name.
And while all of this progress is truly exceptional, at the end of the day, the human brain is still the best inference engine out there. The human brain does an exceptional job of taking small datasets, using common sense, logic algorithms and thought processes, and making inferences about the external world every single day. Furthermore, the brain also has a built in autopilot in terms of the autonomic nervous system regulating the sympathetic and parasympathetic systems (i.e. all the bodily functions that you do not have to actively think about).
The best AI learning algorithms work on the premise of big data. The more data you throw into an algorithm, the better it becomes over time. At the end of the day, it's a form of statistical regression, statistical nonlinear regression, requiring a huge amount of data investment in order to funnel into the algorithm to fine tune the results over time. Generally speaking, that’s how the best AI works.
If the human brain analogy is extended, from the perspective of biological construct, AI has done a great job in terms of mimicking the human brain in terms of axons, neurons, dendrites, firings, activations, and funnelling big volumes of data to flow through to get the right weights via backpropagation, and to ultimately get to a final result. But is this really mimicking the way that the human mind works? And if one looks a little bit upstream, one also must realize that the data inputs into the human brain are themselves uniquely structured.
While we've mapped the way our brain functions and have designed the best analytical algorithms based on that premise, we actually haven't done a great job in terms of how we contextualize data in the first place.
Every single day, we're taking data inputs, and we as human beings, store that data in terms of a context, a contextualized Data Fabric or a data network, establishing relationships between one data point and another in order to create patterns, even in the storage of the data itself. So relationships between datasets exist, at the very same time that we're filing that data as information in our brain.
And that's kind of the second psychological barrier that we need to break. The first being the way that we value data and the fact that data needs to be viewed as a network in terms of its value. And the second point is how we use that data, how to view that data as a network, even when we're talking about storing it, and using it for these types of AI algorithms that are going to change the world. So those are the two barriers. And they both point to data as a network.
Fundamentally, all of this brings us to the point of the failings of where we are in terms of society, and what we really need to do in order to power this next Industrial Age. Everyone has talked about digital transformation. Everyone talks about Industry 4.0. And now the new discussions are around this concept of the Social & Industrial Metaverse. But how do we make this really happen?
And we would argue that we really need to think about data at its core and fundamental level in a different way in order to power the Social and Industrial Metaverse of the future. It starts with data fabrics. And it starts with data as a network. And it starts with re-architecting the way we work and the way we store data in order to enable everything else we want to do.
We have never had these types of conversations until today. The conversations that we've been having around digital transformation is how do we make value of data, the ROI of the data, the use cases that are derived from the data, but no one has actually taken a hard look and asked “Are we even structurally setup within our current organizations, in order to make best sense of that data today?” That's a different conversation.
And it's going to be a conversation that enables the next wave of digital transformation, Industry 4.0, and the Industrial Metaverse as we know it.
Web3 & Metaverse Roadmap and Strategy Forward
Web3 & Metaverse Strategy: How companies can get started to avoid the failures of “digital transformation”
The key steps in developing a concrete roadmap towards industrial digital transformation for companies as it relates to a Web3 and Metaverse strategy can vary depending on the specific goals and needs of the organization, but generally, it may involve the following steps:
Assessing the current state: This includes identifying the current capabilities and limitations of the organization in terms of technology, processes, and skills, as well as understanding the business drivers and challenges that digital transformation is intended to address.
Defining the target state: This involves setting clear goals and objectives for the digital transformation effort, as well as determining the specific technologies, processes, and skills that will be required to achieve those goals.
Developing a roadmap: This involves creating a detailed plan for how to get from the current state to the target state, including specific milestones, timelines, and resources required.
Implementing the roadmap: This involves executing the plan, including acquiring and deploying the necessary technologies and processes, training and upskilling the workforce, and integrating the new capabilities into the organization's operations and business models.
Measuring progress and adjusting the roadmap as needed: This involves tracking progress against the plan and making adjustments as needed to ensure that the organization is on track to achieve its digital transformation goals.
There are several reasons why companies have struggled to achieve digital transformation across business models, operations, and customer experience. These can include:
Lack of leadership support and commitment: Digital transformation often requires significant organizational change and can be disruptive to existing processes and systems. Without strong leadership support and commitment, it can be difficult to get the necessary buy-in and resources to move forward.
Lack of clear goals and roadmap: Without a clear understanding of what the organization is trying to achieve and how it plans to get there, it can be difficult to make progress.
Complexity and complexity of change: Digital transformation often involves integrating multiple technologies and processes, which can be complex and challenging to implement.
Resistance to change: Change can be difficult for some individuals and teams, and resistance to change can be a major obstacle to digital transformation.
Limited resources: Digital transformation can require significant resources in terms of time, money, and personnel, and organizations may struggle to allocate the necessary resources to support the effort.
Overall, the success or failure of digital transformation in Fortune 500 companies as they chart out their Web3 and Metaverse strategies will depend on a variety of factors, including leadership support and commitment, clear goals and roadmap, the complexity and scale of the change, and the availability of resources to support the effort.
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