Looking at the list, we see new editions like Clubhouse and Strava. Partially due to the quarantine, you're still seeing an increase in digital cash transfers with tools like Venmo, an increase in e-commerce shops like Shopify, and an increase in (you guessed it) collaboration tools like Zoom or Microsoft Teams.
Just to pick out some of the key figures in the chart this year.
Amazon users spend $283,000
6M people shop online
TikTok users watch 167M videos
and, Zoom hosts 856 minutes of webinars.
Before 2020, I already thought that big tech had a massive influence on our lives. Yet, somehow this past year has pushed their impact even higher.
One other thing this chart also helps put into perspective is the rapid rate of adoption. As you look at different year's charts, you can see how quickly apps have become part of the cultural zeitgeist.
How do you think these numbers will grow or change in 2022?
Rapid growth means little time to create adequate rules. Everyone’s jumping to own more data than the next and to protect their data from prying eyes.
The trading industry has undoubtedly experienced the rapid growth of data, but it’s pervasive in every industry – and in our personal lives as well.
Having basic data and basic analytics used to be enough, but the game is changing. Traders used to focus on price data, but now you’re seeing an influx of firms using alternative data sets to find an edge. If you’re using the same data sources as your competitors and competing on the same set of beliefs, it’s hard to find a sustainable advantage. Understanding the game they’re playing (and the strategies and rules they follow are important), but now that’s just the table stakes.
Figuring out where you can find extra insight, or make the invisible visible, creates a moat between you and your competition – and lets you play your own game.
I shot a video in 2019 about Data as fuel for your business. Check it out.
It is interesting to think about what’s driving the new world (of trading, technology, AI, etc.), and that often involves identifying what drove the old world. History has a way of repeating itself.
Before e-mails, fax machines were essential. Before cars, people were pleased with a horse and buggy.
These comparisons contextualize the importance of data in today’s new world of economics and commerce.
While not the best comparison, people often default to comparing data to oil.
Since the Industrial Revolution, petroleum has played a pivotal role in human advancement. It fueled (and still fuels) our creativity, technology advancements, and a variety of derivative products. There are direct competitors to fossil fuels that are gaining steam, but it’s more interesting to compare petroleum to data due to their parallels in effect on innovation.
The process of pumping crude oil out of the ground and transforming it into a finished product is far from simple, but anyone can understand the process at a high level. You have to locate a reservoir, drill, capture the resource, and then refine it for the desired product (e.g., heating oil, gasoline, asphalt, plastics, etc.). Then, you have to move it to where it’s needed.
The same is true for data.
You've got to figure out what data you might have, how it might be useful, you have to figure out how to refine it, clean it, fix it, curate it, transform it into something useful, and then how to deliver it to the people that need it in their business. And even if you've done this, you then have to make people aware that it's there, that it's changing, or how they might use it. For people who do it well, it's an incredible edge. – Howard Getson
Data can be seen as the fuel to the information economy and oil to the industrial economy. The amount of power someone has often correlates to their control of and access to resources … and diminishing or diluting those resources can lead to extreme consequences.
Why Data Is Better Than Oil
The analogy works, but it’s just that, an analogy. The more you analyze it, the more it falls apart. Unlike a finite resource (like oil), Data is all around us and increasing at an exponential rate. So the game is a little different:
Data is – ultimately – a renewable resource. It’s durable, it’s reusable, and it’s being produced faster than we can process it.
Because it’s not a scarce resource, there’s no urge to hoard it – you can use it, share it, transform it, infinitely knowing that it won’t diminish. Data is more valuable the more you use it.
As the world’s oil reserves dwindle (and renewable resources grow in popularity and effectiveness), the relative value of oil drops. It’s unlikely that will happen to Data.
Also, while data transport is essential, it’s not expensive in the same way that transporting oil is. In fact, it can be transported and replicated at light speed.
While petroleum can be transformed into many different products – the variety of data-related products and sources is basically infinite in comparison.
On Alternative Data
In the past, investors relied on information and experience from their real lives, from counterparties, and from fastidious attention to CNBC and stock tickers.
While the games, the rules, and the players have all changed, the goal hasn’t… more alpha … more money … more reliably.
Alternative data, to most, means tracking Twitter and Facebook sentiment, but confining your definition to that limits potential alpha.
New sources of data are being mined everywhere, and are letting investors understand trends “before they happen.”
For example, mobile devices, low-cost sensors, and a host of new technologies have led to an explosion of new potential data sources to use directly for predictive insight or indirectly to help improve models.
In addition, private company performance, logistics data, and satellite imagery are becoming popular data sets in a data scientist’s alpha creation toolbox.
There are often concerns about the cost and completeness of these datasets, but as we get better at creating and using them, both will improve.
It also doesn’t always have to be confidential or challenging to find information. Traders now have access to vast amounts of structured and unstructured data. An important source that many overlook is the data produced through their own process or the metadata from their own trades or transactions.
In the very near future, I expect these systems to be able to go out and search for different sources of information. It's almost like the algorithm becomes an omnivore. Instead of simply looking at market data or transactional data, or even metadata, it starts to look for connections or feedback loops that are profitable in sources of data that the human would never have thought of. – Howard Getson
In a word of caution, there are two common mistakes people make when making data-driven decisions. First, people often become slaves to the data, losing focus on the bigger picture. Second, even the most insightful data can’t predict black swans. It’s important to exercise caution.
The thing about “sustainable alpha” is that while one might be able to achieve it, you can’t expect to have it doing the same thing everyone else does (or by doing what you’ve always done).
Markets change, and what worked yesterday won’t necessarily work today or tomorrow. Trading is a zero-sum game, and as we move toward the future, this only gets more apparent.
The future of Data is bright, but it’s also littered with potential challenges. Privacy concerns and misuse of data have been hot button topics, as have fake news and the ability of systems to generate misleading data. In addition, as we gain access to more data, our ability to separate signal from noise becomes more important.
The question becomes, how do you capitalize on data, without becoming a victim to it?
For the past 80~ years, John D. Rockefeller was the undisputed richest person in history, but now Elon Musk is giving him a run for his money with a $340 billion net worth.
Many historians estimate Rockefellers' inflation-adjusted net worth to be around $340B, which would tie them.
While this is fun to look at, realistically, it's hard to get accurate numbers for many of these people. Think how much land Genghis Khan held or that Augustus Caeser essentially owned an entire empire. The calculations of their wealth are very subjective, and records from ancient eras are very scarce.
It is, however, surprising to see someone amass such personal wealth in an era with much more competition.
Do you think someone will surpass that wealth soon?
It is possible. How and why? Because we live in interesting times!
Freeing humans to do more has always been a boon to society. Electricity put a lot of people out of work … but, look what it made possible.
We'd be naive to think AI isn't going to influence the job market, but that doesn't mean you can't navigate that shift.
A Look At Industrial Revolutions
The Industrial Revolution has two phases: one material, the other social; one concerning the making of things, the other concerning the making of men. - Charles A. Beard
There are several turning points in our history where the world changed forever. Former paradigms and realities became relics of a bygone era.
Today, we're at another turning point.
Tomorrow's workforce will require different skills and face different challenges than we do today. You can consider this a Fourth Industrial Revolution. Compare today's changes to our previous industrial revolutions.
Third industrial revolution – The world wide web and computers connect the world together, heralding the digital age.
Each revolution shared multiple similarities. They were disruptive. They were centered on technological innovation. They created concatenating socio-cultural impact.
The fourth shares all the same hallmarks.
We're harnessing new technologies like AI, the IoT, renewable energy, and the blockchain. Automation will reach new levels in this revolution. But there also will be an explosion of new fields, new markets, and new necessary skillsets – it's going to impact the world as holistically as electricity did.
How will humans create value in an increasingly automated world?
I believe that, if managed well, the Fourth Industrial Revolution can bring a new cultural renaissance, which will make us feel part of something much larger than ourselves: a true global civilization. I believe the changes that will sweep through society can provide a more inclusive, sustainable and harmonious society. But it will not come easily. - Klaus Schwab
One of the distinctions I've recently made about the industrial revolutions is that for a long time, technology helped humans act like robots. Think about a plow and a farmer, or a seamstress and a sewing machine. After that, it helped robots try to act like humans, which you've seen more recently.
I believe we're at another inflection point, where new capabilities will free up humans to be more human and to pursue more of what they really want.
Robots can do many things, but they've yet to match the creativity and emotional insight of humanity. As automation spreads to more jobs, the need for management, creativity, and decision-making won't go anywhere … they may be bolstered by data analytics, but they won't disappear.
Our uniqueness and flexibility rightly protect our usefulness. AI and automation free us up to be our best selves and to explore new possibilities.
In that article, I also briefly identified the global chip shortage as a supply chain issue impacting millions of businesses, which could also become a significant barrier to businesses adopting A.I. at scale.
Let's talk a little bit more about the scale of the shortage.
Chips (or semiconductors) are used in substantially all the world's electronic devices – and the more complex machines can require not only more chips … but also more complex chips. For example, a modern car can have anywhere from 500-3000 chips in them.
When the pandemic hit, consumer demand shifts meant that semiconductor manufacturing had to slow down – and a foreseeable consequence of those actions presents us with the inconvenient truth that scaling back production can take up to a year-and-a-half. With demand increasing, the supply vs. demand ratio is getting more out of whack.
Luckily for you, semiconductor manufacturers prioritize the more lucrative goods (like smartphones and other consumer electronics), but that means that it will be harder for small businesses to get them – and it's also severely impacting the automotive industry.
You'll notice that the most affected brands have more production in North America. The reason for that discrepancy is that U.S. manufacturers depend heavily on chips from Asia. The Senate has recently approved $52 billion in subsidies for N.A. chip manufacturing, which hopefully will lessen that dependence over time.
We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:
How do we use AI effectively?
Many people misuse AI. They think they can simply plug in a dataset, press a button, and poof! Magically, an edge appears.
Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations. And even before that – they haven't even properly assessed whether AI is needed in their business. Remember, AI is a tool, not the goal.
Even though this is the golden age of AI … we are just at the beginning. Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.
Do you remember Maslow's Hierarchy of Needs? Ultimately, self-actualization is the goal … but before you can focus on that, you need food, water, shelter, etc.
In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive.
Artificial Intelligence and Data Science follow a similar model. Here it is:
First, there's data collection. Do you have the right dataset? Is it complete?
Then, data flow. How is the data going to move through your systems?
Once your data is accessible and manageable you can begin to explore and transform it.
Exploring and transforming is a crucial stage that's often neglected.
One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.
The data stream from exchanges isn't perfect.
Consequently, using real-time market data as an input for AI is challenging. We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible. Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).
Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things … and then begin to train your data.
Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it.
That's the foundation to allow you to start model creation and optimization.
The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).
If you put garbage into a system, most likely you'll get garbage out.
I recently shot a podcast with Mike Koenigs about taking your ideas and transforming them not just into products but into platforms. It was also featured on Forbes.
Many of the most valuable companies (like Tesla, Apple, and Amazon) leverage platforms to scale past their initial products and create profitable ecosystems.
The video is 50+ minutes – but covers the topic in great depth, and Mike adds a lot of significant distinctions. I think you will like it.
Since recording this podcast, I've continued to make finer distinctions.
One such distinction, to help businesses plan around new technologies, was to ask two key questions.
What technologies that already exist are going to impact your industry the most in the next 3-5 years?
What technologies that you expect to exist are going to impact your industry the most in the next 5-10 years?
I ask these questions because adopting new technologies doesn't mean you have to invent something new. It can mean capitalizing on existing technologies and finding new ways to use them. Understanding what is "likely" lets you lean in the right direction and helps you visualize the most likely paths forward.
This helps you figure out where to spend focus, time, energy, and other resources. Remember, it is easier to follow and leverage a trend, rather than to fight it.
Since the beginning of time, humans have been confronted with disruptive new technologies. While technologies continue to change, human nature has remained relatively stable. As a result, predicting human nature is often easier than predicting technology.
So, rather than trying to predict what technologies will win, you can focus on which needs and capabilities are most likely to attract attention and resources. Innovation and technology will follow to satisfy the desire.
Knowing that, the question is what can you build that leverages your unique abilities and the likely path of your chosen market.
It sounds simple, but it's a powerful distinction and potential differentiator between you and your competitors.
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