- Michael Moesgaard Andersen on how to accelerate growth by way of data-driven innovation
- How do you sustain a business model in a data-driven economy?
- What are your thoughts on the future acceleration and growth of data-driven innovation?
- How does a data-driven innovation model provide a competitive advantage?
- What makes a data-driven business model with data-driven innovation successful?
- A new method of creating value
Throughout his career, Michael has authored and co-authored several academic books and articles, creating a link between research and his practical business experience. In these publications, he often draws on the experience from his portfolio companies.
Michael is currently working together with Torben Pedersen, professor at Bocconi University, on a textbook on data-driven innovation.
Michael Moesgaard Andersen on how to accelerate growth by way of data-driven innovation
In this interview, Michael answers questions related to data-driven innovation, how to sustain a business model in a data-driven economy, how a data-driven innovation model provides a competitive advantage, and what makes a data-driven business model with data-driven innovation successful?
How do you sustain a business model in a data-driven economy?
We have learned through the Blitzscaling experience that speed is of essence.
Speed brings something new to the table when we discuss innovation. In the 1960s, the average lifetime of S&P 500-companies was 60 years. Today it has decreased to 20 years and continues to shrink.
The five most valuable companies in the year 2000 – General Electric, Exxon Mobil, Pfizer, Citigroup, and Wal-Mart are no longer among the top 10. Today’s top five are relatively new companies like Amazon, Google, Apple, Tencent, and Microsoft.
Therefore, speed is of essence and often overlooked among well-known theorists like, for example, Michael Porter and his framework of driving forces. Porter has taught many MBA students that it is important to initially choose a profitable industry to enter. However, many start-ups tend to think differently, as they see a lack of profitability as an inroad to achieve success through real innovation.
Speed is the driver of data-driven innovation
Coming back to the most important point, speed is of larger importance than whether you adopt one strategy or another.
Airbnb is a good example of the importance of speed.
There have been many imitations of Airbnb like platforms. However, just by focusing on speed, Airbnb has managed to establish a global market-leading position. Without the focus on speed, that wouldn’t have happened. Instead, you would have seen more locally-oriented platforms emerge.
What are your thoughts on the future acceleration and growth of data-driven innovation?
On the acceleration of data-driven innovation
There is no final and definitive answer. However, on the issue of acceleration, it is a common body of knowledge that being data-driven in your business model goes together well with what we call Network Effects.
For example, multi-sided platforms carry with them Network Effects on the user side and the customer side, and on the supplier and demand side, depending on the business case.
There is exponential growth value carried hand in hand with the Network Effects. It is similar to what we call the n x (n - 1) formula, meaning that if you have one customer or one user there is nobody to communicate with. If there are two you have one string of communication. When there are 10 then you arrive at 90 communication possibilities, increasing exponentially to 380 when you reach only 20 customers.
On the growth of data-driven innovation
When looking at the growth of a company, you can use the four V’s. Volume, Visioning, Velocity, and Value.
The first three are determinants of growth, which I expand on in my upcoming book.
Volume is somewhat similar to the Network Effects.
Visioning is also interesting; are you interested in “man-on-the-moon” innovation or innovation with marginal effect?
And when it comes to velocity, the speed at which you can establish a data-driven business case is incredibly important in this line of work. Who can scale at speed? And who can get what we call the “first-mover advantage”?
And then, of course, the last is the value. If your data is validated and of sufficiently high caliber, great value can be derived from it.
If you have established a multi-sided platform with a salient network effect, you can continue adding services “on top” of what you are already delivering. You see this within the ecosystems of Google, Facebook, Tencent, and Alibaba. Big data environments create new services and revenue streams made possible with AI.
How does a data-driven innovation model provide a competitive advantage?
Michael Porter also applies here. Competitive advantage does not carry the same importance as it did in a slower economy.
To respond directly to your question, you may have a first-mover advantage but nobody knows whether you will have a long-term, sustained competitive advantage.
Sustained competitive advantage, in my view, was relevant when development took place at a slower pace and when speed was not of the same essence as today. But today, realistically, it is very difficult to establish a long term competitive advantage. Therefore, strategic theorists, like Rita McGrath, now talk about transient advantages rather than long-term sustained competitive advantages.
[A transient advantage is a business strategy accepting of the short-lived nature of competitive advantage. This “innovation” strategy works to continuously create new advantages.]
And we are addressing this in uncharted waters. If you look at the world’s largest companies years ago - as mentioned earlier the likes of General Electric, Exxon Mobil, Pfizer, and Wal-Mart – they were typically dealing with and/or dependent on physical products and physical assets.
Currently, the most valuable companies in the world are data-driven companies. That is Google, Alibaba, and Tencent, who are not dependent on physical assets. We can even argue that the core value of Amazon is more tied to the data-driven platform than to the physical assets. Although Amazon has a physical component, their “first-mover” advantage is primarily in being a data-driven company.
We have seen these companies emerge as data-driven but we do not have sufficient data to see how competitive advantages will work in the long run.
What we have seen with Margrethe Vestager (European Commissioner for Competition) and at other regulatory environments, is that regulators will try to - I wouldn’t say unbundle Facebook, Google and the likes - but at least restrict their dominant power to some extent. This may mean their first-mover advantage withers away or breaks down. It may also work in opposite effect, strengthening and further consolidating their situational monopolies.
What makes a data-driven business model with data-driven innovation successful?
There are some key characteristics.
Old paradigms adopted closed innovation models, which were characterized by a linear way of thinking in that they took last year’s performance level as the basis and then adjusted figures marginally across the entire organization. Moreover, this also went together with the use of analog techniques and without taking advantage of big data opportunities.
Today, you have different kinds of open innovation. You move from linearity to singularity and from analog to digital, or exponential growth as seen with the singularity movement.
That is a very interesting development as we are struggling academically to describe what is currently happening with innovation. Our notion of open innovation may become somewhat outdated.
Today, new transformers entering the market and shifting the conversation. What we see with the Valuer.ai innovation platform is a new type of innovation that does not fit into the existing conversation of closed vs. open innovation.
It is a new paradigm that is emerging, a next-generation model of open innovation.
To make it short, you reverse the “innovation pyramid”.
Usually, under the closed and open innovation model, you innovate for the benefit of the users and to the users. Today, Valuer.ai and others are reversing this innovation pyramid and include users in a dialectic co-creative space. With this shift, you see the former very strict distinction between producer and consumer wither away.
You see that in the case of Valuer and startups both being users and co-creators. And you see that with large corporations being both users and co-creators. You reverse this innovation pyramid and arrive at a more crowd-based system. It is far different from some decades ago with a few people sitting in a research and development organization in their ivory tower inventing something that might be transformed into a new product.
A new method of creating value
We are looking at a new way of creating innovation and value. If you don’t create value out of your strategic innovation, it does not matter. In the forthcoming book, we work with a formula where we get closer to an understanding of what real innovation could look like. We work with two methods.
1. Ideas x execution x adoption
If innovative ideas or innovation itself do not reach the market it does not matter as such. Innovation is not just an idea as people thought in the good old days. It is ideas x execution x adoption.
If you have zero in one area, you will gain nothing. If you cannot execute, or cannot get the adoption of the innovation, you will not be able to create real value.
2. Understanding the semantics of innovation.
What does the semantics tell us? The root explanation is that innovation is something new. But how can you innovate when you have your boundaries and blind spots?
We are working with the notion of blind spots. How you can avoid and how you can convert blind spots into actual knowledge and thereby create real (new) innovation. In a sense, you become aware of - and can therefore handle - your innovation process in your business model.