Problems often involve complex solutions, yet history tells us that breakthrough ideas come from our ability to question simple things.
I have built many highly complex novel algorithms for applications such as removing noise from cellular signals, accelerating mobile encryption and putting "automatic lending" on the blockchain. Complexity has its place. Some problems are just hard, messy and - yes - complicated.
What is the hardest subject you can think of?
Invariably, most folks will think of something related to science, and quite rightly. Whilst we like to eulogize fashionable entrepreneurial heroes like Musk, Jobs et al, our lives are more tangibly impacted by the work of scientists, most of whom we will never here from.
Who invented the MRI scanner? Who invented the flu vaccine? Who invented the combustion engine?
For that matter, who invented the iPhone? Most of the technology comes from places outside of Apple.
But complexity is often a feature of the implementation, not the fundamental nature of the problem. Science, although it involves highly complex ideas and procedures, is often driven by simplicity.
Much of our inventive history is a quest to escape our biology. We came into the world by a process of evolution that shaped our brains to do things that we can't remember doing, like running through the wilderness.
Along the way, we encountered things that we accepted as just the way they are, like objects, when free of any visible support, tend to fall to the ground.
For centuries, this simple occurrence was explained by Aristotle's account that objects have a natural place to which they want to return. In the case of steam, it rises to its natural place in the clouds. In the case of an apple, it falls to reside where it belongs (in order to rot and return to the earth): on the ground.
Newton allowed himself to be puzzled by this simple occurrence. In his account of the world, objects moved only under the influence of forces. If this is true, which he believed it to be, then the only answer, to be consistent with his laws, is that it must be subject to a force: a simple explanation. The complexity, of course, is that the force is invisible. This he could not explain, and no one has since.
Allowing one's self to be puzzled by simple things is the powering force of science and innovation. Indeed, it is the only way that science progresses. We like to think that scientific progress is thanks to the so-called scientific method. This is not how discovery happens. It is only confirmed or denied by the method.
Indeed, others more loftier myself, like Max Planck, have argued that science only really progresses "one funeral at a time", by which he means that we only manage to shed incorrect ideas once the scientist holding the idea dies and makes room new ideas. Let us not forget that for all of his genius, Einstein refused to accept much of the quantum account of the universe and possibly held up physics as a result.
Returning to gravity, we now readily accept the presence and power of invisible things, like the radio signals tethering our mobile phones. And so it is easier to imagine ideas that involve invisibility.
That said, it might well be that we haven't understood invisibility at all and it is blinding us to new explanations: simple ones.
In my work as a technologist, the progress of innovative ideas follows a similar path of discovery to science, even though CxOs often use the word "science" in a pejorative sense: "We don't want to pay for science experiments."
Allowing one's self to be puzzled by simple things reveals many possibilities. For example, when I was asked by Art.com to find new ways to innovate with the online sale of wall decor (art) I found that people could not really describe the kind of art they wanted, but could say: "I know it when I see it."
We can all relate to this simple idea, but it turns out to be deeply puzzling with even a superficial level of scrutiny. Indeed, why we like anything is, on some levels, a mystery.
Innovation often flows when we allow ourselves to be puzzled by such simple things and then ask "Why" questions instead of accepting conventional explanations. A favorite example of mine is how Parker Pens only increased sales once they realized that their pens had higher utility as gifts than they did as writing instruments. But the consultant who suggested this new way of seeing was initially ridiculed for thinking along such lines instead of "pen technology" complexities, whatever they might have been.
That consultant allowed himself to be puzzled by a very simple thing: Why do people buy nice pens?
But there is a deeper force at work inside of companies that makes seeing simple things even harder. It is the "cult of the expert" disease whereby we compete to seem like experts even if we are not. The irony is that folks who are truly expert tend to realize how little they know, not how much. Imposter syndrome is common amongst the highly erudite, not the ignorant.
In competitive environments where expertise is often the currency of exchange, versus the art of getting results, it can be hard to ask simple questions. The tendency is to want to appear current and trendy with "advanced" ideas, so much so that the mere notion of looking for simple answers is dismissed. Again, the irony is this is where true experts often find the best answers.
Of course, this problem is exacerbated by the common management tool of shunning simple questions. The number of techniques for doing so are too many to mention, but mostly amount to the same thing: "I say so."
But if we require any evidence that so-called experts can really get it wrong, then we need look no further than the lofty realms of the Nasa rocket scientists. The fabled story of how a billion-dollar satellite project failed is staggering: one team measured in inches whilst the other in centimeters. Could it get any simpler?
I am usually hired by CxOs to do two things:
1. Define a technology vision
2. (Optionally) Execute on the vision
In practice, I am often asked to do both, but the first can be done as a standalone exercise.
At some point in the conversation, somebody, usually me, utters the phrase "technology company". Quite often, I might assert that my client is NOT a technology company. This can cause offense. But such a bristly reaction is usually a sign of a meaningful conversation versus the all too common conversation with zero content.
I therefore created the following diagram to explain what I mean by a technology company. The definition is hardly canonical - you might have your own. As with all terms, defining them helps.
As with all graph-looking diagrams, we should first check the axes to make sure we understand them.
The horizontal axis here means the value of an asset. I prefer to use asset-based thinking in such a conversation because it usually resonates with a CxO. Other parameters, like "competencies" can become too vague too quickly.
The vertical axis means the know-how in leveraging the asset - do we know how to make money from it?
Let's begin on the left where things are easier to understand and less ambiguous or contentious for most CxOs.
Entrepreneurs - Unknown Knowns
If we know the value of an asset, say a cellular network, but do not yet know how to make money from it, then it is the job of an entrepreneur to solve that problem. In the case of a cellular network, the asset might be its ability to determine a users location. It is the job of the entrepreneur to find a way to monetize this asset - i.e. to create a business model. Innovation here is mostly business model innovation, but there are myriad other types of innovation (that you might find on Geoffrey Moore's innovation types schema).
Engineers - Known Knowns
Let's say the strategy or marketing team figure out how to monetize location data in the network, say by selling it to retailers who use it to predict footfall. They hand the task to engineering to build functions that can execute the business model. Engineers know how to extract value from the asset because they have the necessary infrastructure to ping the location of users. They might chose to build a solution or buy one from a vendor, or both.
Engineers use a lot of technology to build functions, perhaps including cutting-edge technology, like, say, AI or cloud-based data lakes. They might well account for the lion-share of fixed costs in the company, as is typical for any digital company. This is what causes CxOs to characterize their company as a "technology company". To me, this is bit like calling Walmart a finance company because they handle so much money.
Let's turn then to the role of technologists...
Technologists - Known Unknowns
It turns out, after some reflection, that the strategy guys determine how to make $$$ from users' locations if the location data is available in 3D (for urban areas). They hand the problem to engineering who declare that the current infrastructure has no such capability even though the biz guys can make $$$ from it. Furthermore, no vendor claims to have a solution either (although they might make such claims, as vendors often do).
It is the role of a technologist to invent the solution by taking available resources, such as the existing network capabilities, and augmenting them with some capability that requires invention.
This step has all kinds of approaches and considerations that we won't elaborate here. The point is to understand that a technologist brings an asset into existence that we know the value of but don't currently known how to extract its value.
This activity could be a one-off event or, more strategically, could be part of a systematic attempt to expose new values that support key themes. In this case, "advanced/creative location finding" could be a theme that inspires investment into an R&D team whose job is to expose a number of novel location methods, not just 3D location.
Scientists - Unknown Unknowns
If you already work in an R&D company, then the job of scientists should be clear. However, let's elaborate for the rest of us and also clear up a common confusion about science:
1. The job of a scientist is often mistaken for something mechanical rather than creative because of confusion about the so-called scientific method.
2. There is a new type of science that is possibly valuable to all companies, whether they conduct R&D or not. It is called Data Science.
The job of a scientist (within the above rubric) is to be highly creative within a certain framework. We have a cellular business that we mostly use to connect people and things (IoT). Unknown to us, the network has a hidden capability, let's say to detect the spread of ideas (via voice calls). A scientist might discover this capability via a number of means. It requires advanced techniques not only in speech analysis, but in the creation of "thought vectors" (yes, there is such a thing) to indicate ideas. Perhaps the scientist first has to postulate and define what an idea is (in order to detect one).
Critically, there has been no request from technologists, engineers or entrepreneurs for the discovery of an "ideas network" capability. But it turns out that such a capability is worth $$$$$ in the realm of digital marketing.
Of course, to discover such a capability requires investment in a team whose job is research. This is often beyond many companies because the scope of ideas includes invention of methods that require significant capital to bring about. In this case, the network might require a massive upgrade with new voice-processing circuits.
However, many companies increasingly find that they are sitting on a proverbial gold-mine of untapped assets that are potentially extractable at relatively low cost. These assets are data. But that's another conversation!
So what is a technology company?
I hope that the definition is now easy to understand. A technology company uses technologists to extract new value from assets where the value is not accessible using current engineering solutions. Let's stick to that definition here as an understandable explanation. It has lots of nuances and caveats, but they don't affect the overall gist of the definition.
To give another example from my own list of such endeavors, I was asked by McLaren (the racing guys) to suggest how they could leverage their remote sensing expertise (used on F1 cars) to build a business. One option might be to provide a platform for other racing companies, or say fleet companies, to manage the network of remote vehicular sensors and apply performance processes to the data (e.g. optimization of some asset).
But I described a vision in which they might create a "Performance Sensing Platform" that any company could use to build performance-enhancing solutions atop of sensing networks. For example, Nike could build sensors into training shoes and then offer athletic customers a performance-monitoring service of some description.
This was the vision part of the exercise.
I then moved on to architect the system in a way that enabled new value to be extracted from sensors by enabling mathematical algorithms to be applied in real-time across millions of sensors - i.e. on a scale far exceeding what they had previously engineered for F1 racing. Such a solution had $$$$$ value but no available engineering solution. In this case, I invented novel architectural techniques to orchestrate the data and algorithmic processing at scale.
Does a company have to be a technology company?
Of course not. But returning to my aside about data science, then the answer is increasingly: yes. Or, rather, it will pay to adopt some of the techniques of a technology company in order to generate future value in a sustainable fashion, especially in the digital realm where, increasingly, the value of a company is in the value of its data, but not just its known value!