What is a Technology Company (Part 1)
And why it matters.
I have often been hired by companies who call themselves technology companies, but are not technology companies in any meaningfully useful definition of the word.
It would be like Whole Foods calling themselves a computer company because they happen to use computers.
So what is a technology company, and why does it matter? Well, it depends on what kind of company we’re talking about. I want to eliminate from this post (for a later post) those companies that are unmistakably technology companies because they make a technical product, like, say, Intel. But what if you’re a Verizon (US) or John Lewis (UK) who rely heavily upon technology, but — and here’s the key — mostly obtain the tech via technology vendors, like Oracle or SAP.
If you identify with that type of tech-vendor-centric operation, then this post might be useful in helping to understand how you might become a technology company, and why.
I am usually hired by CxOs to do two things:
1. Define a technology vision
2. (Optionally) Execute on the vision
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 easily cause offense, especially when a large chunk of capital and opex goes on technology.
I therefore created the following diagram to frame the conversation. The framing is not canonical – you might have your own – but it helps to arrive at some useful conclusions by couching the job of technology within a kind of economic, or utility, framework.
As with all quadrant 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 executives who like to evaluate or score their company in terms of its assets, tangible and intangible. Other parameters, like “competencies” are sometimes useful, but can become quite vague all too quickly.
The vertical axis means the executable knowledge available to leverage an asset – put simple: do we know how to make money from it?
Let’s begin on the left where things are easiest to understand and uncontentious for most executives, especially the top left (so that is where we will start).
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 assets are any of the technical capabilities. For example, one asset is clearly the ability to make a call. Another might be the ability to determine a users location. It is the job of the entrepreneur to find a way to monetize such assets – i.e. to create a business model that turns the delivery of the asset into revenue.
Innovation here is mostly business model innovation, but there are myriad other types of applicable innovation (that you might find on Geoffrey Moore’s innovation types schema). Another example might be marketing innovation. So, in the case of O2 in the UK, they pioneered the concept of event sponsoring via rebranding the former millennium dome to be called The O2. This was a fantastic example of marketing innovation.
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 physical retailers who use it to predict footfall (which is a real use case exploited by Telefonica).
They might hand engineering the task of building 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, but the scope and outcome is known, such as “we need a means to count how many times a partner uses the location platform so that we can bill them for each time they use it.”
Engineers use a lot of technology to build functions, even 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 function is often what causes executives 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. Well, Walmart might well be a finance company, but you get the idea.
Let’s then turn to the role of technologists so that we get closer to the mark of being a technology company.
Technologists – Known Unknowns
Returning to our carrier example, let’s say that it turns out, after some analysis, that the strategy guys realize they could make way more $$$ from users’ locations if the location data is available in 3D (for urban-canyon areas). They hand the problem to engineering who declare that the current infrastructure has no such capability even though the biz guys can make $$$ from it. Furthermore, no vendor claims to have a solution either (although ambitious vendors might make such claims, as vendors often do, but intend to build the tech once they have the contract).
It is the role of a technologist (whether in the company or a vendor) to invent the solution by taking available resources, such as the existing network capabilities, and augmenting them with some capability that does not yet exist.
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 (i.e. how to make money) but don’t currently known how to extract its value – i.e. we don’t know how to get 3D location data from our network.
This invention activity could be a one-off event or, more strategically, 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.
It is important to understand here that the type of step we are talking about most likely has the following characteristics:
- Game changer – the company can leapfrog their competition who are reliant upon vendors (who don’t have the 3D capability)
- Software economics – especially the ability to scale its use with minimal incremental capital costs
- Intellectual property rights – the opportunity to defend the competitive advantage against in-category competitors
- A positive affect on shareholder value and analyst commentary
In fact, depending upon the nature of the invention, there are all kinds of “software economics” or “digital economics” benefits that significantly multiply the company’s ability to exploit its user base, and potentially to expand it.
Scientists – Unknown Unknowns
If you already work in an R&D company, then the job of scientists should be clear even though it is my view that science is a poorly understood set of activities. Indeed, most of us never learn science per se, only its outcomes (e.g. the laws of physics or chemistry) and its modes of measurement (e.g. measuring pendulum swings or calorific burn rates).
Let’s make some claims 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.
Well, I have lied a bit there as I don’t consider Data Science to be a science at all, but it has some usefully similar methodological approaches for the sake of our current discussion.
The “job of a scientist” (within the current rubric I want to explore here) is to be highly creative within a certain framework, or scope. Let’s return to our carrier network. We have a cellular business that we mostly use to connect people and things (via IoT). Unknown to us, the network has a hidden capability, let’s say to detect and in some way measure the spread of ideas (via the content of calls and messages). However, this is just a hypothesis. There is no such capability of an existing network and nor do we know how to make money from it.
However, for strategic reasons, say related to the future of communications as being envisaged as: “the connection of ideas (not just people)” — and a latent belief that anyone who can tap into such a capability could be in a powerful position (strategically speaking). Strategists have taken a view that such a capability is potentially worth $$$$$ in the realm of digital marketing.
A scientist might discover this capability via a number of means.
It might require advanced techniques, not only in speech analysis, but in the creation of “thought vectors” (yes, there is such a thing) to indicate ideas in the sense of intentionality (per the technical use of that term). Perhaps the scientist first has to postulate and define what an idea is (in order to detect one). From there they might develop techniques that could lead to the creation of an ideas network.
Critically, there is not yet any project for technologists, engineers or entrepreneurs to engage in the utility of an “ideas network” capability.
Of course, to discover such a capability requires investment in a team whose job is research. This is often beyond the reach of many companies because research involves methods that either require significant capital to bring about results or a willingness to take a certain type of risk. And we should point out the use of the term type of risk. Often we treat all types of risks as if they are equal. They are not. Indeed, there is a huge difference between risk in the technical sense (some numerical probability of success or failure, say) and the operational sense (translating the risk into a strategic move).
If you’re company isn’t yet in the realms of creating technology, then it’s unlikely to become an R&D company any time soon.
Well, there is one important caveat: data science.
Although I protested that this activity isn’t really a science (at least as practised in many organization where a better term might just be analytics), if approached like a science, then it can yield tremendous benefits with potentially reasonable R&D investment. The method I am describing is similar: i.e. to discover unknown uknowns in data. For example, upon analysis of our carrier network logs, it might be that we discover a kind of “idea network” proxy that whilst not providing the $$$$$ opportunity envisaged, it might nonetheless provide a significant competive advantage in personlized marketing, say.
However, most companies don’t quite have the infrastructure nor talent to undertake this kind of data science activity, often because their data assets are poorly maintained or otherwise inefficient. I shall return to this in a later post.
So what, then, is a technology company?
A technology company uses technologists to extract new value from assets where the value is not accessible using current engineering solutions. Let’s stick to that definition here as an understandable and meaningfully plausible explanation. It has lots of nuances and caveats, but they don’t affect the overall gist of the definition.
To give another example from my own list of projects, I was asked by McLaren (the racing guys) to suggest how they could leverage their remote sensing expertise (used on F1 cars) to build a business. One option might be to provide a platform for other racing companies, or say fleet companies, to manage the network of remote vehicular sensors and apply performance processes to the data (e.g. optimization of some asset).
But I described a vision in which they might create a “Performance Sensing Platform” that any company could use to build performance-enhancing solutions atop of sensing networks. For example, Nike could build sensors into training shoes and then offer athletic customers a performance-monitoring service of some description.
This was the vision part of the exercise.
I then moved on to architect the system in a way that enabled new value to be extracted from sensors by enabling mathematical algorithms to be applied in real-time across millions of sensors – i.e. on a scale far exceeding anything they had previously engineered for F1 racing or that could be reasonably obtained from technology partners. Such a solution enabled $$$$$ value by facilitating solution sellers to discover new business opportunities and by using engineers to do their job of turning a new asset into revenue in the manner set out by the entrepreneurs.
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!
Moreover, and this is the key part to be mulled over, the methods of technical invention are increasingly available to a wider audience via the availability of cloud services like AWS and the massive power of transferable knowledge via open source software projects. The economics of software and technical invention can be exploited by companies with an existing strong set of capabilities in an existing market. This makes for exciting, interesting and lucrative futures for existing “non technology” companies.