At the core I am an entrepreneur. I love new technologies that positively impact humanity, systems learning, and I am a huge fan of successful business people with a soul. I live for "ah-hah" moments, and making vision reality.
“When I think about creativity, it is always in relation to a foundation. We have our knowledge. It becomes deeply internalized until we can access it without thinking about it. Then we have a leap that uses what we know to go one or two steps further. We make a discovery. Most people stop here and hope that they will become inspired and reach that state of “divine insight” again. In my mind, this is a missed opportunity. Imagine that you are building a pyramid of knowledge. Every level is constructed of technical information and principles that explain that information and condense it into chunks… Once you have internalized enough information to complete one level of the pyramid, you move on to the next. Say you are ten or twelve levels in. Then you have a creative burst… In that moment, it is as if you are seeing something that is suspended in the sky just above the top of your pyramid. There is a connection between that discovery and what you know—or else you wouldn’t have discovered it—and you can find that connection if you try. The next step is to figure out the technical components of your creation. Figure out what makes the “magic” trick.”
I love this philosophy on marketing from Rory Sutherland. Rory is the Vice Chairman of Ogilvy & Mather Group, and I heard this on Invest Like The Best (Source).
First of all, don’t think that marketing is marcoms. Because is very many businesses the two are conflated to a point where marketing is sometimes dismissed as “the coloring-in department”.
Marketing should basically be the repository for any psychological insights, particularly those which are counterintuitive, or which run against normal economic assumptions in the space of human perception and behavior. That would apply also, by the way, to internal relations, shareholder relations, and obviously customer relations.
So marketing, in a sense needs to step back up to the plate and not allow itself to be trapped in the marcoms/communication “ghetto”. The second thing is, by being trapped in that ghetto, [marketing] tends to get framed as a cost; as a necessary evil, and not a source of value creation. Once you’re in that marcoms ghetto, you’re essentially a cost center. As a result, what you get judged by is the efficiency with which you perform the necessary. This turns the whole thing into this sort of marcoms insanity, where everybody is trying to target 2% more efficiently every month.
And I would argue that a large part of marketing needs to be both experimental and probabilistic. We shouldn’t turn it into this efficiency/optimization game.
…Maybe, actually you should do very inefficient mass advertising, simply because it increases your odds of getting lucky. You can’t predict how in advance, and you can’t attribute the success in retrospect, but yet nonetheless, fame is valuable, on balance. More lucky things happen to famous people simply because more people have heard of them.
…that’s the first thing [for new clients]: don’t turn into an efficiency/optimization game. Accept that it’s probabilistic. And accept that there need’s to be a trade-off between exploit and explore.
This quote is from Justin Rosenstein, Co-Founder of Asana, One Project, which I heard on the Netflix documentary The Social Dilemma.
We live in a world in which a tree is worth more financially dead than alive; in a world in which a whale is worth more dead than alive. For so long as our economy works in that way, and corporations go unregulated, they’re going to continue to destroy trees; to kill whales; to mine the earth; and to continue to pull oil out of the ground, even thought we know it is destroying the planet, and we know it is going to leave a worse world for future generations.
This is short term thinking based on this religion of “profit at all costs” as if somehow magically, each corporation acting in its selfish interests is going to produce the best result.
This has been affecting the environment for a long time. What’s frightening, and what hopefully is the last straw that will make us wake up as a civilization to how flawed this theory has been in the first place is to see that now, we’re the tree. We’re the whale. Our attention can be mined. We are more profitable to a corporation if we’re spending time staring at a screen; staring at an ad, than if we’re spending that time living our life in a rich way.
And so we’re seeing the results of that. We’re seeing corporations use powerful artificial intelligence to outsmart us and figure out how to pull our attention towards the things they want us to look at, rather than the things that are most consistent with our goals, and our values.
When Margaret Thatcher was prime minister, she knew she was going to have to confront the miners’ union in a long a bitter struggle. In 1981 they went on to strike for a pay rise. Mrs. Thatcher immediately made enquiries about the size of coal stocks. She wanted to know how long the country could survive without new supplies of coal. As soon as she discovered that stocks were low, she in effect conceded victory to the miners. She then, very quietly, arranged for coal to be stockpiled. The result was that when the miners went on strike again in 1983, she resisted their demands. There was a prolonged strike, and this time it was the miners who conceded defeat. A battle she could not win in 1981 she was able to win in 1983.
I finished watching The Last Dance for the first time a couple of months ago. This week I started watching it again. The well-curated, bespoke quality, behind-the-scenes look at the rise of Michael Jordan and the legendary 1990s Chicago Bulls is inspiring to the core. Watching the series is perhaps one of the single best studies on how humans can harness and develop talent to become great, and while watching, the inner desire for greatness burns inside the ambitious viewer, as if the series contains some sort of ‘strive for greatness’ contagion.
A single question has stuck with me while watching. That is, what game am I playing? One of the things I find fascinating about the Michael Jordan story (which can perhaps be extended to all great athletes, more generally) is the hyper-focused and continued strive for excellence within the framework of a simple goal. That’s “the Game”, so to speak. When meditating on this, I can’t help but consider how often I over-complicate my own life ambitions, and how much I crave a simple goal—a simple “game”. For Michael, it was to score baskets and win championships. Score the most baskets and win the most championships. That’s where he invested his energy; his consciousness. That was his game.
Between the first time I watched the series and now, through a few different events, conversations, and just keeping my head down and working on this question, I’ve gratefully been able to hone knowing what my game is. And now that I know, I don’t want to hedge. Optionality is great within the game, i.e. I can “break left” or “break right”, but playing multiple games can only distract. Wake up in the morning and be the best. Maybe it’s really that simple.
We live in a digital world which pulls us into conflating the urgent and the important. As marketing decision makers we need to build up our prioritization and decision-making muscles. We need to constantly rebucket “nice to have” and “need to have”. And we need to constantly realign our activities with business objectives, which can be all the difference between calling something a “success” or a “failure”.
What’s all the rage with data science? And what exactly is data science? In this post, I can’t promise to answer either of those questions in a way that will be satisfactory to you… ha! What I will share is what excites me about the field’s study and why I think its applications will become an increasingly important part of my work from here on out.
Data science: it’s a thing.
As you can see in the above trend, the term “data science” as a topic has exploded in interest over the past decade. In an oft-quoted 2012 HBR article by academic Thomas H. Davenport and DJ Patil, the former Chief Data Scientist of the United States Office of Science and Technology Policy, the role of the data scientist will be the “sexiest job of the 21st century“.
Data science as the ego-killer
On a personal level, and regardless of my own data science skills (at present I’d consider myself an amateur and aspiring promoter of the discipline at best), I’m excited about data science because I believe that it is far more than just a “sexy job”. I believe that data science is becoming an increasingly pervasive force that is shaping the way we as individuals and groups make sense of the world around us. I’ve witnessed this force begin to emerge in business and capital markets, and I hope that it will also positively impact governments and societies on a much larger scale in the future.
I see data science as the pursuit of truth; its practice being to seek out and pay attention to the voices of empirical evidence — not those voices which scream loudest. To leverage data science is to seek influence, not power. Data science is changing the way we persuade, and are persuaded by others. I believe that “data-driven decision making” should be as much of an educational requirement as algebra is today, perhaps more so (reference: Annie Duke’s Alliance for Decision Education!).
Ben Jaffe, host of the Linear Digressions podcast wonderfully articulates a worldview and approach that relies on data science, explaining, “…we [all] have cognitive biases… logical weaknesses… pride… and that’s why we have things like the scientific method and statistical rigor: to compensate for our deficiencies as humans. And one of those deficiencies is that we see ourselves as neutral, even though just like the algorithms we build, we reflect the world that we are steeped in.” (Note: I expanded Jaffe’s quote beyond its original context, which was about proactively lessening racial discrimination through the use of data.)
Data informs how (not what) you build
Please don’t get me wrong. I believe that data and experimentation support vision, but do not replace vision. Data science itself requires creativity, but does not replace creativity. And specific to business, I’d argue that qualitative based vision is absolutely a requirement of any successful venture, but that there are many ways to build towards vision. And that holds true especially when an entrepreneur is working to achieve something that hasn’t been done before.
I started my career in construction management — working on teams that oversaw complex multi-million dollar projects, where architects, designers and their clients dreamed up details of how a physical space should look, feel, and be built. We’d be handed huge stacks of architectural drawings, specification binders, and material samples dictating nearly every detail of a project. At times it felt like a real life, more challenging version of building a Lego set (with the primary challenge being the ever complex variable of people management).
But outside of construction, manufacturing, and a select few other fields, life and progress do not have the luxury of moving forward with so many knowns. In company-building, for example, the chief executive may have a very clear vision of what they want to accomplish (in terms of business milestones, for example), and perhaps of certain aspects of which path they want to take (in terms of organizational structure and culture, for example), but will not have detailed plans and specs on how to go about building towards that vision.
I think this experience of transitioning from construction management (an environment with instructions) into technology startups (an environment without instructions), has significantly contributed towards my attraction to data. Not knowing what to do or what to build can be disorienting, and as organizations continue to move away from top-down hierarchies, it’s becoming increasingly important to find better ways to lead, manage and build. Whether in the discipline of online customer acquisition, my specialty, or some other aspect of company-building, learning to ask smart questions and looking for answers in the Data provides the closest thing to instructions.
What’s the difference between data science and other “data stuff”?
Over the past six years as a growth marketing practitioner, I’ve definitely come a long way in understanding how to use data in my every day work, in campaign and funnel analysis, shaping marketing experiments, crafting data-infused educational content, and in making discoveries of data patterns that help inform strategy. Has any of that been data science?
In the Davenport and Patil article I mentioned above (the one about data science being sexy), here’s how those guys define the real stuff: “More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.” (Emphasis my own)
When referencing that definition of data science and asking myself if my data-related work in the past as a marketing professional has been “real” data science, I ask myself, was my fourth grade science fair project “real” science? Hell yes. I’m proud of what I accomplished in fourth grade science. So too am I proud of the “data science light” work that I’ve done over the past several years, even if the work led to relatively minor value-adding insights. That said, from here on out I expect to that I will require far more intention on a personal level, surrounding myself with a high caliber of talented people well-versed in data science, increasing my fluency in various aspects of the field, in order to deliver insights of substance and significant value. And to me, progressing along this path and knowing that I’ll be able to add more value is exciting.
Let’s get philosophical (again)
Back to my point about data science as the pursuit of truth… I believe that to succeed in data science in any capacity, even if (or especially if!) it’s just working in an organization that pays attention to its data, a deep level of self awareness and humility is required. When seeking out answers to a question, so many of us think we know, want to be one that knows, and convince ourselves of some supposed truth. These motives (be it for money, status, or other) and unconscious biases lead to a place of overconfidence. My definition of overconfidence is any degree of confidence that comes from thinking we know something that we don’t, and that can be a drug. It feels good, and it might win material reward and popularity contests, but it can also lead to systematic failure. Daniel Kahneman discusses this at length in his Nobel-prize winning work, Thinking, Fast and Slow. While this is not a self-help book, an implicit takeaway from it might be that by training ourselves to be more analytical and deliberate in judgement, we can make better decisions and lead happier lives. The study and application of data science, I believe, lies at the very core of this training.
I think it’s also important to avoid analysis paralysis. Data science isn’t for every situation, and an important part of what can make the applied science great is knowing where to apply it. Additionally, there’s a cost to collecting more data and running analyses— oftentimes it’s better to make a decision with inaccurate data, partial information, or no data at all. But even then, wouldn’t it be awesome to have the ability to know when to walk away from the data, or the potential answers the data might provide? How many of us are confident in saying “I don’t know; let’s look at the data”? How many of us build confidence through inquisitiveness; through not knowing an answer? As someone who’s spent a lot of time drumming and studying the drumming greats as a teenager, there’s an analogy I can’t resist here, which is that the best drummers know how to play softly and when to leave empty space.
The next big thing: doing data science in your underpants
I don’t believe that data science will ever provide all the answers we seek, but I predict that in the coming years it will help us unlock puzzles and challenges in mind-blowing ways that are currently unimaginable. I believe that data science will help clarify realities that may have been previously concealed. And in terms of data science being a gold rush of sorts, I think that it will actually be part of a new software revolution, in which companies won’t have to adapt their problem-solving needs to a particular program, but rather the program will adapt to the needs of the company. I believe that data-driven companies will far outperform their non data-driven counterparts.
Jose Quesada, founder and CEO of Data Science Retreat, on the SDS podcast, talks about why data science is going to be the next big thing: “The next [technology] wave… machine learning, makes websites and apps look ridiculously underpowered by comparison. We can build machines that can see the world. That can identify objects. That can understand your language as you interact with things like Alexa. This amount power is actually in the hands of anybody with a computer. So you can literally be at your kitchen table with a laptop, running a deep-learning model detecting something that is needed for you to solve a problem, because of open source libraries and pretrained models. You can literally be in your underpants or at your kitchen table, solving really important problems that were impossible to solve just five years ago.”
Here here to making data more useful, and to the pursuit of our refined individual and collective character through the practice of data science! As enthused as I am to publish this post and tell the world why I’m excited about data science, I’m even more excited to actually do the work. Surely I was overconfident about one or more things in this piece — something which I thought I knew but will soon be kicking myself for being too sure of. I guess that’s what subsequent posts are for.
I heard the following thoughts on friction and process from Tobi Lutke, co-founder and CEO of Shopify, on the podcast Invest Like The Best. I find Tobi to be extremely insightful and include him on my list of favorite business people. Although the context of this quote was related more specifically to organizational design and company culture, I think the concept is more broadly applicable, and can be used to think about how friction relates to any human interaction, like customer experience as an example:
The concept of friction is probably the most powerful underacknowldged force in the world of business and products in general… think about process in general… you can actually separate process into two different things:
There’s process that makes something previously impossible possible
Or makes something that was previously possible 10X easier
Both are good processes. But that’s a set. Anything that doesn’t accomplish either of those two things is bad process. The only reason why you have that process is because you’re trying to get people to act in some meaningful way different from their intuition. It’s not about, “hey, [this is] enabling something new”… it’s about, “I would like you to do this particular thing… in that particular way because that makes it easier for me”. But by that you’re actually creating an un-intuitive process for everyone, and somehow we’re okay with that.
Leaders, if they are wise, think about the impact of their decisions many years from now. Famously, when asked in the 1970s what he thought about the French Revolution in 1789, Chinese leader Zhou Enlai replied: “Too soon to say.”
Imagine you have total power. Whatever you say goes. Then one day you decide to share your power with nine others. You now have at best one-tenth of the power you had before. Now imagine that you have a certain measure of influence. Then you decide to share that influence with with nine others whom you make your partners. You now have ten times the influence you had before, because instead of just you there are now ten people delivering the same message.
Power works by division, influence by multiplication. Power, in other words, is a zero-sum-game: the more you share, the less you have. Influenceis a non-zero-sum-game: the more you share, the more you have.