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!