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  • The Real Business We’re All In …

    I’ve given a few speeches recently and have new subscribers to our weekly commentary (click here to sign up), so I thought it was a good time to write about the importance of data.

    I revisit this topic about once a year because it’s important. 

    The Hidden Engine: Why Data Fuels Innovation

    Technology and innovation are popular topics, but people often ignore what makes it all possible …  the hidden foundation,  data.

    Data is the lifeblood of modern businesses and the fastest-growing resource we have.

    The quest to find and use data has created a modern-day “Wild West.” While AI is often positioned as a “Gold Rush,” data is the precious resource powering the race.

    Another way to look at it is that data is the ammunition used by today’s tech titans in their battle for dominance.

    In either case, it is easy to see that data is a scarce and valuable resource.

    The Data Deluge: Finding Signal in the Noise

    We’re living in an age of data explosion.  Every day, a staggering 328.77 million terabytes of data are created, amounting to an estimated 120 zettabytes of new data by year’s end.

    Video is a significant driver, but so is the Internet of Things, which is growing more than 15% annually.  There are now almost 20 billion connected devices, and that number will continue to grow. 

    This rapid growth presents a challenge.  Tech giants like Alphabet, Amazon, Apple, Facebook, and Microsoft all hold unprecedented data troves, creating a race for ownership and control.  Regulations struggle to keep pace with this digital stampede.

    Rapid growth means little time to create adequate rules.  Everyone’s jumping to own more data than the next and to protect that data from prying eyes.

    As a great example of this, I often warn people to keep their intellectual property off of ChatGPT or other hosted language models. 

    But here’s the real concern: Are we losing sight of the signal in all this noise?  Just having vast amounts of data isn’t enough.  The true value comes from extracting meaningful insights – the nuggets of gold buried within the data avalanche.

    To Do The Impossible, Make The Invisible Visible

    Collecting basic data and using basic analytics used to be enough, but it is not anymore.  The game is changing. 

    I also see it trading, but it’s pervasive in every industry and our personal lives as well. 

    For example, traders used to focus on price data … but there has been an influx of firms using alternative data sets and extraordinary hardware and software investments 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 edge. 

    Understanding the game others are playing (and its rules) is important.  However, that’s only table stakes.

    Figuring out where you can find extra insight or where you can make the invisible visible creates a moat between you and your competition and lets you play your own game.

    Here is a quick high-level video 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.), which often involves identifying what drove the old world.

    Decoding the New World: Data as the Catalyst

    Understanding today’s driving forces – like AI – often involves examining what propelled past eras.

    History has a way of repeating itself.  Even when it doesn’t repeat itself, it often rhymes.

    Before e-mails, fax machines were amazing.  Before cars, people were happy with horses and buggies.

    The key to unlocking new economic realities lies in fresh perspectives.

    In this new world, new or better data is often the game-changer.  It’s the alternative dataset that allows us to approach challenges and opportunities from entirely new angles.

    Before data analytics, businesses relied on intuition and limited information.  Now, data empowers us to see patterns and make data-driven decisions, propelling innovation at an unprecedented pace.

    These comparisons help explain the importance of data in today’s new world economics.

    One of the more recent shifts is in the value of synthetic data. 

    SymtheticData
    via Gartner

    Synthetic data can mimic the statistical properties of real-world data, making it useful for a variety of purposes.

    For example, synthetic data can be used to train machine learning models when collecting traditional data is impractical or presents privacy concerns.  It is also used in various other applications, such as data privacy, testing and development, data augmentation, simulation and modeling, risk assessment and management, and enhancing data quality.

    You don’t have a competitive advantage if you use the same data and the same process as other people.  That’s why understanding how to recognize and capture synthetic data is important.  It can shift your perspective, add dimensionality, help you solve different problems, and create transformative results.  

    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

    While data is the foundation, it’s about transforming your data into actionable insights. 

    By identifying your real business, the KPIs of success, and what data you’re underutilizing, you can massively improve the efficiency and effectiveness of your business and create new products that transform your future. 

    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.  This is the same mistake people make with AI.  Both are tools, not the end goal.  Second, even the most insightful data can’t predict black swans.  It’s essential to exercise caution and prepare for the unexpected. 

    The future of data is bright, but it’s also littered with potential challenges.  Privacy concerns and data misuse are hot-button topics, as are 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.

    One of the biggest problems facing our youth—and really all of us—is how much information is thrust at us every waking moment of the day.  No previous generation has had this much access to data.  As a result, many are actually less informed than in the past.  Soundbites become the entire news story, and nuance gets lost in the echo chambers. 

    The question becomes, how do you capitalize on data without becoming a victim of it? 

    Food for thought!

  • The Jobs Most Impacted By AI

    As we talk about the proliferation of AI, it's probably helpful to see where it's predicted to have the most impact. 

    Job_Departments_Impact_by_AI_sitevia visualcapitalist

    These results come from a World Economic Forum report. 

    In context, large impact refers to full automation or significant alteration. Small impact refers to less disruptive changes. 

    IT and finance have the highest share of tasks expected to be "largely" impacted by AI … which is unsurprising. 

    We've also already seen the impact of LLM and generative AI on customer service and customer care. As these tools improve, more cases will be able to be fully handled by AI. 

    This chart isn't meant to make you feel afraid that your industry will be automated—it's meant to help you understand what tasks you should consider automating. 

  • Applications Of Data Analytics & AI For Your Business

    It's a common theme in entrepreneurial discussion these days … AI is coming for your jobs. 

    The more nuanced statement is that AI isn't going to take your job – but someone using AI better might. 

    Recently, Andrej Karpathy, ex-director of AI at Tesla and founding member of Open AI, posted a great tweet about how software engineering will be automated.  He compared it to automated driving. 

    With automated driving:

    1. first, the human performs all driving actions manually
    2. then, the AI helps keep the lane
    3. then, it slows for the car ahead
    4. then, it also does lane changes and takes forks
    5. then, it also stops at signs/lights and takes turns
    6. eventually, you take a feature-complete solution and grind on the quality until you achieve full self-driving.

    The progression is similar for software engineering (and, you guessed it, your business as well)

    1. first, the human writes the code manually
    2. then, GitHub Copilot autocompletes a few lines
    3. then, ChatGPT writes chunks of code
    4. then, you move to larger and larger code diffs
    5. then, a tool starts coordinating other tools (a terminal, browser, code editor, etc.)

    You get the point.  Human oversight begins to move towards increasingly higher levels of abstraction and management. 

    If you think about it, this parallels a pretty generic path that a typical employee might take in your business.  A junior employee can't handle any ambiguity.  As they move up, a mid-level employee can probably handle some mild ambiguity … they need to know where they're headed, but they don't need hand-holding on how to implement it.  A senior employee needs to know what problems they need to tackle, and then you get to entrepreneurs, and they don't even need to know what problems to tackle … they'll find some. 

    Evolution

    This suggests a pretty solid modus operandi for the coming years.  If you're worried about being replaceable, focus on higher-level behaviors. 

    AI empowers businesses to do more with less.  Early adopters of AI will gain a significant competitive advantage by automating tasks, enhancing customer experiences with personalized recommendations, and making data-driven decisions that lead to cost savings and increased revenue.  Integrating AI into your business will propel your organization forward by unlocking new levels of efficiency, effectiveness, and certainty.  If you're steering the ship, you don't need to be as afraid of the waves. 

    Here is a framework I created to identify the path to some not-so-easy wins that lead to sustainable business growth and progress: 

    • Create Process Playbooks that leverage automation and AI to help businesses exceed standards both front-stage and backstage.  This class of solutions improves practical and business outcomes and helps avoid errors, omissions, and discretionary mistakes.
    • Use Outcome Integrity Trackers to log decisions, actions, and results, hopefully improving and standardizing processes and outcomes.  This capability will evolve into the ability to measure the difference between skill and luck reliably and to the creation of accurate recommendation engines with real-time expectancy scoring.
    • Capture, Calculate, and Curate Custom Metrics.  Much of what happens each day is lost.  Finding a way to save this data creates, expands, and augments a valuable new asset that is valuable itself, helps solve complex problems, and leads to new products, services, and solutions.
    • Curate a Single Integrated Source of Trusted Data that is accurate, complete, and up-to-date.  Together, that data becomes the foundation for building new models, metrics, validations, certification, and compliance solutions.

    Developing a Comprehensive AI Strategy is Crucial for Business Success

    Businesses that don't adapt to changing landscapes fail. Having a roadmap, centered on what doesn't change is a reliable life support. Change doesn't have to be dramatic to be valuable. Just by taking these little steps and asking the right questions, you can make a big impact. I hope you're finding way to reap the rewards of these transformations, not just surviving them. 

    Message me if you want to talk more about this.

  • Overhyped Technologies (Or Not)

    Just because something is overhyped doesn’t mean it’s bad.

    Gartner’s Hype Cycle is a great example of this concept.  It highlights the likely cycle of inflated expectations, disillusionment, and, ultimately, utility.

    The key takeaway from the Hype Cycle model is that much of what happens is predictable … and that a significant portion of the extreme swings are based on human nature rather than technical merit.

    Haters are going to hate, and sometimes a fad is more than a fad.  For example, here is a front-page article from the New York Times in 1879.  It questions the utility of electric lights as a replacement for gas-powered lighting.  In case you were wondering, that one might have been a bright idea.

     

    Screen Shot 2022-05-15 at 8.45.33 PM

     

    The point is that humans have proven themselves to be pretty bad at exponential thinking.  We’re not bad at recognizing periods of inflection, but we often have trouble recognizing the consequences of the change (and the consequences of those consequences) and predicting who the winners and losers will be as a result of those regime changes. 

     

    Screen Shot 2022-05-15 at 2.26.23 PM

     

    There are countless examples.  Here’s a funny one from Maximum PC Magazine in 2008.  It shows that hype isn’t always a sign of mistaken excess.  This list purported to show things that were getting too much attention in 2008.  Instead of being a list of has-beens and failures, many of these things rightfully deserved the attention and hype they were getting.

     

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    It’s been over 15 years since this came out.  How did the predictions hold up?

    Apple has become one of the world’s biggest and most successful companies (with a market cap approaching 3 Trillion dollars).  The iPhone has sold over 2.2 billion phones and accounts for over half of Apple’s total revenue.  Meanwhile, Facebook has become Meta and is also one of the biggest and most successful companies in the world (with a market cap of well over a Trillion dollars).  And the list keeps going: HD video, 64-bit computing, downloading movies from the internet, and multiple GPU video cards. 

    Take just that last one. Nvidia has been the primary beneficiary of GPU growth, and it is one of the highest-performing stocks of the past few decades (with a market cap of well over 2 trillion dollars). 

    It’s hard to believe how poorly this image aged. 

    Remember that the trend is your friend while it continues.

    Just because something is overhyped – doesn’t mean you shouldn’t be excited about it. 

    The key is to stop thinking about the thing that’s being hyped and, instead, to start thinking about how to use things like that to create what you really want.

    Onwards!

  • Velocity Versus Speed

    Recently, I've been thinking a lot about how businesses scale and technology adoption accelerates.

    For example, consider how fast AI is improving and transforming business.

    Last year, I shared a short video called Speed Matters. It includes some thought-provoking ideas. You can view it by clicking here.

     

     

    While speed matters, faster is not always better.

    As you focus on doing more things faster, it becomes more essential to give people room to do important things slower.

    You've almost certainly heard the phrase, "It's better to measure twice and cut once."  It's much easier to do something the right way from the beginning rather than trying to fix it after you mess it up. 

    Activity does not create progress if it doesn't move you in the right direction.

    This reminds me of a distinction my friend Nic Peterson makes. What you want in your business is velocity rather than speed. Velocity implies a vector (and preferably only one vector). You want to move fast in the desired direction, not fast towards distractions, mistakes, or money down the drain. 

    To add one more layer to this, there's an "almost true" axiom in technology: you can only have two out of these three things: a project done fast, done right, and done cheaply.

    But it's only almost true for a big reason. If you've already built the team and put in the work, replicating that work for new projects can be done fast, right, and comparatively cheaper. 

    Are you just moving fast, or do you have velocity in your business?

    Something to think about … 

  • Checking Back In with Dr. Doom

    In 2018, while in New York for work, I was invited to a party that ended up being a pretty unique experience. 

    The rules were that for the first hour it would be first names only, no discussion of what you do, etc. Part of the fun was figuring out who was there and why they were special. And, there were a lot of pretty impressive people in the room. As I was wondering who was able to bring all these experts and thought leaders into one home for a house party, I found it was Nouriel Roubini – the infamous Harvard economist known as Dr. Doom. 

    Nouriel Roubini's predictions have earned him the nicknames "Dr. Doom" and "PermaBear" in the media. He predicted the housing bubble crash in 2007-2008, and has extensively studied the collapse of emerging economies. 

    So, after a tumultuous few years for the global economy, I thought I'd check back in and see what he was saying. 

    It turns out he's less pessimistic than you would guess. He's pretty optimistic about 2024 growth and not particularly worried about a recession—though he is expecting a downturn. 

    He also thinks there's a possibility that growth remains above potential, and inflation remains sticky. That would be good news for the economy, but bad news for markets – as the Fed likely wouldn't cut as much or as soon as people are hoping for. 

    Now, Nouriel has been wrong before, and I don't trust any singular pundit. My mindset is to listen to voices that don't already believe what I do. I tend to be optimistic as a rule, and I've been optimistic on things like blockchain, whereas Nouriel has been staunchly negative.

    But, he's a smart and educated voice who can justify his opinions. And I end up more educated – and often modifying my stance a little bit – based on the context he's able to give. 

    By the way, as I was editing this post, I saw that Chase CEO Jamie Dimon and billionaire hedge fund founder Ray Dalio admit they got warnings on the US economy wrong — for now.

    Are you listening to voices outside of your preferred channels?

  • Warren Buffett’s Lifetime Legacy

    Warren Buffet is 93 – and he just released his 2023 annual letter.  Berkshire Hathaway saw record profits of $97 billion last year.  If you scroll to the bottom of this year's letter, you can see he's got an almost 20% CAGR since 1965 and an overall gain of 4,384,748% since 1964.  That is hard to imagine.  It's even harder to do. 

    I think part of it comes from how grounded in reality he is.  He focused on what doesn't change instead of what does and on who his "actual" competition is. 

    “Berkshire should do a bit better than the average American corporation and, more important, should also operate with materially less risk of permanent loss of capital. Anything beyond “slightly better,” though, is wishful thinking. This modest aspiration wasn’t the case when Bertie went all-in on Berkshire – but it is now.” – Buffett on Berkshire's prospects for shareholders like his sister, Bertie

    Buffet is not looking for lottery tickets; he's stacking little wins.  There's power in that.  He also pointed out that as stock trading has become more accessible – it's made daily buying and selling easier, but also more erratic.  That, unfortunately, benefits the "house" more than these individuals. 

    Warren Buffett is a legend for many reasons.  Foremost among them might be that he's one of the few investors who clearly has an edge … and has for a long time. 

    From 1976 to 2017, his Sharpe ratio (excess return relative to risk) was approximately double the overall market.  He even did well in 2021.  Berkshire Hathaway passed a trillion in assets last August (up from $700 billion in 2019) – and is still performing well. 

    While many people consider Buffett to be an investor, I also consider him to be an entrepreneur.

    At the age of six, he started selling gum door to door.  Obviously, selling gum wasn't the key to his path to riches.  So, how did he make his first million?  Here's a video that explains it.

    via Coolnimation

    He made his first million at age 30 (in 1960).  For context, a million dollars in 1960 would be worth about $10.4 million today.

    Buffet has always been honest about his bread-and-butter "trick" …  he buys quality companies at a discount and holds on to them.

    It is fascinating to recognize how much the world has changed – and yet how much it has stayed the same.

    For one last bonus, here's one of my favorite quotes from his 2011 letter. 

    “Money will always flow toward opportunity, and there is an abundance of that in America. Commentators today often talk of “great uncertainty.” … No matter how serene today may be, tomorrow is always uncertain.

    Don’t let that reality spook you. Throughout my lifetime, politicians and pundits have constantly moaned about terrifying problems facing America. Yet our citizens now live an astonishing six times better than when I was born. The prophets of doom have overlooked the all-important factor that is certain: Human potential is far from exhausted, and the American system for unleashing that potential – a system that has worked wonders for over two centuries despite frequent interruptions for recessions and even a Civil War – remains alive and effective.

    We are not natively smarter than we were when our country was founded nor do we work harder. But look around you and see a world beyond the dreams of any colonial citizen. Now, as in 1776, 1861, 1932 and 1941, America’s best days lie ahead.”

    Further, the Pragmatic Capitalist highlights this lesson:

    Nothing stopped so many innovators and entrepreneurs more than the fear of failure.  If you allow yourself to be constantly scared into thinking that the world is doomed you will never take that risk which might result in great reward.  And perhaps worse, if you never fail you will never learn to get up, brush yourself off, move on and succeed in the future.  This does not mean you should wander through this world with great complacency and blind optimism, but if you deny yourself the ability to maximize your full potential, you will always come up short.

    Fear, uncertainty, and greed are all hallmarks of every year.  There are undoubtedly ebbs and flows to everything, but we are clearly marching towards greener pastures. 

    A great lesson to learn from Buffet is that the game isn't about the next year or even three years … it's about a lifetime and the generations that come after us. 

    Let's continue to make our tomorrows bigger and better than our today. 

    Onwards!

  • Here Are Some Links For Your Weekly Reading – February 18th, 2024

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    Here are some of the posts that caught my eye recently. Hope you find something interesting.

    Lighter Links:

    Trading Links:

  • Going With The Flow: Functional Mapping

    There’s a concept in design and transportation called Desire Paths. 

    The desired path is the path that users take despite the intended path by the builder of a community or application. 

    Here’s a great example. 

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    Reddit via itstartswithani

    And, here’s a whole community forum focused on desire paths. 

    It’s often easier to take advantage of human nature … or just nature … than fight against it. 

    To that effect, I shot a short video on how this relates to your business and tech adoption.  I call it functional mapping.  Check it out. 

     

    Understanding the natural path for both technology and your clients makes it easier to understand and anticipate the capabilities, constraints, and milestones that define your path forward.   That means you actually have to understand the different types of users and what they expect to do.

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    Each stage is really about the opportunity to scale desired capabilities and automation.

    It isn’t really about building the technology; rather, it is about supporting the desire.

    You don’t have to get it right.  You just have to create momentum in the right direction.  This means that if you understand what is coming, you don’t have to build it … but you should figure out where you want to build something that will move things in the right direction.

    You’ve probably heard me talk about how Capabilities become Prototypes.  Then, Prototypes become Products.  And ultimately, Products become Platforms.

    This model is fractal.  That means it works on many levels of magnification or iteration.

    What first looks like a product is later seen as a prototype for something bigger.

    SpaceX’s goal to get to Mars feels like their North Star right now … but once it’s achieved, it becomes the foundation for new goals.

    This Framework helps you validate capabilities before sinking resources into them. 

    It helps you anticipate which potential outcomes you want to accelerate.  Rather than simply figuring out the easiest next step … you have to figure out which path is the best next step to your desired outcome.

    The world is changing fast!  I hope you’re riding the wave instead of getting caught in the riptide!

  • How To Make Real-Time Decisions

    I recently came across an interesting technique that fighter pilots use to make fast and accurate decisions in high-stakes situations. 

    The Air Force calls it an OODA Loop.

    It is an iterative feedback model that Colonel John Boyd designed as a foundation for rational thinking in chaotic situations like dogfights.

    It stands for Observe, Orient, Decide, Act. 

    OODA.Boyd

    via Wikipedia

    Why do people use decision models?  Obviously, to make better decisions.  But really, they use models to create a process that avoids many of the mistakes or constraints that prevent good decisions.

    You make countless decisions every day – and at a certain point, you reach decision fatigue.  It can be harder to make decisions when you are tired, after you've made too many, or when the intensity of the environment distracts or drains you. 

    It's one of the reasons I rely on artificial intelligence.  Here are some others. 

    • Best practice becomes standard practice. 
    • It accounts for signal and noise.
    • It attempts to quantify or otherwise make objective assessments, comparisons, and choices. 
    • And, it often gives you a better perspective by letting you apply and compare different models or decision techniques to achieve the desired outcome.

    Nonetheless, many algorithms are dynamic and adaptive automation of processes or strategies that humans have used successfully before.

    So, let's take a closer look at the OODA Loop, which stemmed from analyzing many interactions between and among fighter pilots during battle and training. 

    Observe

    The first step is to observe the situation to build the most accurate and comprehensive picture possible.  The goal is to take in the whole of the circumstances and environment.  It's not enough to observe and collect information … you must process the data and create useful meaning. 

    It's the same with data collection for an AI system.  Ingesting or collecting data isn't enough.  You have to be able to apply the data for it to become useful. 

    Orient

    The second step is less intuitive but very important.  When you orient yourself, it becomes easier to recognize strengths, weaknesses, opportunities, and threats to identify how changing the dimensionality or perspective alters the outcome. 

    This step reconnects you with reality in the context of your cognitive biases, recent decisions, and more.  For example, have you received new information since starting?

    I think of this as carrying a map and pulling out a compass while exploring new lands.  Sometimes, you need to remember where you started, and sometimes, you need to make sure you're going where you think you are. 

    Decide

    The last two steps provide the foundation for taking action. 

    When there are multiple decisions in front of you, observing and orienting help you choose wisely. 

    In business and with AI, you can go through these loops multiple times. 

    Act 

    Finally, remember that the best-made plans mean nothing if you don't act on them. 

    Once you've taken action, you can reobserve, reorient, and keep moving forward. 

    Conclusion

    Like most good mental models, The OODA loop works in many situations and industries.

    Speed is often a crucial competitive advantage.  For example, knowing (and taking decisive action) while others are still guessing (and taking tentative action) is something I call time arbitrage. 

    Said another way, you make progress faster by walking in the right direction than by running in the wrong direction. 

    These processes (and technology) also help us grow more comfortable with uncertainty and uncomfortableness.  Markets are only getting more volatile.  Uncertainty is increasing.  But, when you have the ability to adapt and respond, you can survive and thrive in any climate.