Data-Driven Innovation

Why the Data-Driven Model Will Be Key to Future Success

Michael Moesgaard Andersen, Torben Pedersen

• Publisher: Routledge
• Year: 2021
• ISBN:
  9781000329162
• Edition: 1
• Topic: Business
• Subtopic: Entrepreneurship
• Index: Business

Today, innovation does not just occur in large and incumbent R&D organizations. Instead, it often emerges from the start-up community. In the new innovation economy, the key is to quickly find pieces of innovation, some of which may already be developed. Therefore, there is the need for more advanced means of searching and identifying innovation wherever it may occurs.

We point to the importance of data-driven innovation based on digital platforms, as their footprints are growing rapidly and in sync with the shift from analogue to digital innovation workflows. This book offers companies insights on paths to business success and tools that will help them find the right route through the various options when it comes to the digital platforms where innovations may be discovered and from which value may be appropriated.

The world hungers for growth and one of the most important vehicles for growth is innovation. In light of the new digital platforms from which data-driven innovation can be extracted, major parts of analogue workflows will be substituted with digital workflows.

Data-driven innovation and digital innovation workflows are here to stay. Are you?

chapter 1
Searching for accelerated growth in a data-driven world

Since the beginning of the 2008–2009 financial crisis, major parts of the Western world have suffered from lower growth. Moreover, somewhat satisfactory growth on the global scene has been skewed by the hypergrowth exhibited by the BRIC (Brazil, Russia, India, and China) countries, which was well above the global average, leaving a large number of Western countries far behind with growth figures significantly below the global average. This lack of macroeconomic growth escalated further with the emergence of the COVID-19 crisis in 2020. Global GDP dropped dramatically, and the repercussions of the crisis extended well into the ensuing quarters. In general, stimulus packages involving various combinations of quantitative easing and negative interest rates from the US Federal Reserve have not been sufficient remedies for the challenges in recent years.

On the microeconomic level, many companies, including large global corporations, are suffering from a lack of growth. Consider for instance, General Electric, Exxon Mobile, Pfizer, CitiGroup, and Walmart, which were the world’s five most valuable companies in 2000. Today, they have been surpassed by high-growth companies such as Amazon, Google (Alphabet), Microsoft, Apple, and Facebook from the United States, and Tencent and Alibaba from China. Most of these companies (with the exception of Microsoft) did not even exist in 2000. This leads us to a key question: What characterized the old leaders, and what characterizes the new?

Invariably, the business models of the old leaders were tied to physical products and/or physical assets. The new leaders are IT companies in which growth is driven by data, especially big data. Big data allows for innovation as new services are enabled solely on the basis of data. Consider for example, Google and Facebook, who are masters in converting “thin” data generated on their platforms without cost into “thick” data of high value for advertising, analytics related to consumer behavior, and other types of analytics. While thin data are collected for free, thick data are sold for millions.

In other words, the new leaders are utilizing data innovatively. Another approach is to use data to search for and access innovation wherever you might find it. Approaching innovation in a data-driven fashion is the topic of this book, which focuses on new digital platforms that can deliver such data and help transform innovation processes into digital workflows.

On the macroeconomic level, the term “data-driven innovation” was coined in studies carried out by the OECD.¹ For a number of years, the OECD worked on the basis of the OSLO Manual, which dealt with definitions of various types of innovation. Subsequently, the OECD began to address the combination of innovation and big data, which led the organization to focus on the combination of big data and innovation:

The analysis of “big data,” increasingly in real time, is driving knowledge and value creation across society, fostering new products, processes and markets; spurring entirely new business models; transforming most if not all sectors in OECD countries and partner economies.²

Clearly, the OECD is keen to expose the benefits of data-driven innovation at the macro level. Therefore, the organization has used a great deal of energy to investigate how data-driven innovation can improve productivity and efficiency in the public-welfare sectors of its member countries.

However, our interest is in data-driven innovation at the micro level, which we believe has been largely overlooked. This is regrettable as data-driven innovation represents a new, critical source of growth.

A disproportionately high amount of innovation takes place in the vibrant market for start-ups. Therefore, rather than looking to large companies’ R&D departments in our search for innovation, we examine the collaborative relationship between startups and large corporations.

We believe that large corporations have been overvalued for quite some time, especially when it comes to complacent incumbents. Even though these corporations possess significant resources and scale, many of them are in desperate need of growth. In the wake of the COVID-19 crises, these characteristics become more relevant than ever as the traditional leaders seem to be hardest hit by the crisis. Some of them are also struggling to integrate the UN’s Sustainable Development Goals (SDGs) into their business equations.

Conversely, startups seem to have been largely overlooked, at least by complacent incumbents. Startups often possess superior innovation capabilities and are, therefore, an important source of growth despite the fact that they often lack resources and scale.

As such, there is considerable complementarity between start-ups and large corporations. A natural starting point is to look into the possibilities of establishing collaborations between these two vastly different types of organizations. We do not intend to address all of the relevant subtopics of this collaborative relationship but rather just a very specific subcategory: how to appropriate value from digital platforms

Acquiring data-driven innovation

This topic is probably more important than ever in the wake of not only the recent general recession but also the subsequent COVID-19 crisis and SDG challenges. Growth can help large corporations and, on an aggregated level, society, conquer such issues.

This book deals with the more abstract history of innovation as well as the practical aspects of how one can acquire data-driven-innovation and appropriate value from it.

Our point of departure is our reflection on what innovation is in Chapter 2. The term “innovation” has become a colloquialism for almost anything that needs to be portrayed in a positive light. Who does not want to be innovative these days? A more thorough explanation of some of the key terms in this book, including “innovation,” “data-driven,” “artificial intelligence” (AI), and growth, is offered in Appendix D.

Human minds and human behavior are prone to systematic biases because humans are driven by experience and because they rationalize their behavior ex post. The fact that we also have “blind spots” adds to the challenge of innovation. The same is true for linear thinking, which is built into the human mindset. Linear thinking seems to contradict innovation, which often generates, for example, exponential (rather than linear) growth. In short, innovation essentially goes beyond our own comprehension.

These characteristics of the human mindset have several implications. First, we are often satisfied with business as usual—we are not intuitively minded for extreme growth or for the blitzscaling of companies. Second, we tend to focus on best practice and not next practice. In other words, our focus should be on how we can achieve something without precedence.

Chapter 3 moves us from the individual level to the company level. For many years, innovation was viewed as something that occurred behind closed doors in the R&D departments of large companies. However, this kind of closed innovation is ineffective in today’s world. A typical example is an innovation process that takes place in closed circles over a considerable span of time and eventually fails to meet market requirements. Product-market fit, which is often believed to be perfect prior to the initiation of a product-innovation process, is simply not there when the rubber meets the road. To a considerable degree, this resembles the painful outcome seen when software developers in a closed environment use the “waterfall method” to develop new IT systems. When a new system is ready to go live after several years of development, the outside world has changed so much that the new system does not meet the demands of the receiving world.

For these reasons, a great deal of closed innovation has gradually been replaced with open innovation. Open innovation is characterized by cooperation with external parties. The basic philosophy of open innovation resembles the agile software innovation process in which changes and adjustments take place concurrently in order to arrive at the desired product-market fit. Such innovations can either take the market by surprise early on, which is known as a “black swan innovation,”³ or they can gain a foothold initially and then move up the market. The latter is what Clayton Christensen famously coined “disruptive innovation,”⁴ which refers to seemingly irrelevant innovations that subsequently constitute a major threat to leading firms.

Open innovation has been key for increasing the effectiveness of innovation but has done so at the expense of speed. The increase in effectiveness leads to innovations that better meet the requirements and needs of the market, especially consumers. However, opening innovation processes to include collaborative efforts with external parties such as universities, results in more time-consuming processes. The question then becomes one of how to kill two birds with one stone. In other words, can innovation processes be designed so that they are simultaneously both more effective and more efficient?

A new innovation philosophy is now emerging. Some label it a new generation of innovation and talk about innovation networks in which innovation is generated in conjunction with the “creative commons.”⁵ The idea is that a more open business model based on data-driven innovation provides broader access to the market, lowers the costs of innovation, and allows for risk sharing.

What do these ideas mean in practice? One implication is that it is now possible to buy access to innovation as a digital service. Big data, openness, digital media, and platform technologies are all trends that can be combined to support the advent of advanced platforms from which one can acquire and extract innovation. This creates a new approach to innovation. Should I build or buy? Invariably, a mix may occur, but the bottom line remains the same: Accessing innovation from which you can appropriate value through a digital platform represents an interesting path toward simultaneously ensuring both effectiveness and efficiency in terms of high speed and low costs. The collective term for this new opportunity is “data-driven innovation,” which we will use for the remainder of this book.

Chapter 4 deals with data-driven innovation as the key to solving many of the challenges at hand. The chapter starts with an outline of the traditional build-or-buy decisions that are normal in many industries but are relatively new when it comes to innovation, which is characterized by a strong tradition of “building.” The “buy” option is closely connected with the idea of bringing the difficult start-up/corporation collaboration to new heights through the new digital platforms that are quickly emerging.

A walk-through of these digital platforms is then presented along with a discussion of the extent to which the use of advanced AI has appeared. This culminates in a comparison of two of the leading platforms: CBInsights and Valuer.ai. Both platforms are based on the Software as a Service (SaaS) business model in which one can buy access to innovation through a subscription.

These new possibilities are addressed in detail in Chapter 5 where some of the functionalities are outlined (although some of the technicalities are discussed in the appendices). In addition, Chapter 5 includes some use cases of a “what if” nature, which examine the platforms that may be used under certain circumstances. In some cases, it may be prudent to rely on complementary input from more than one platform. In other cases, input from just one platform may suffice.

Chapter 6 brings us back to the combination of innovation and growth. Despite the fact that this book aims to illustrate the advantages of relying on data-driven innovation, we must remember that growth does not occur automatically, even if you manage to find the right needle in the haystack. The chapter introduces the term “exnovation” as an important condition for delivering growth and, thereby, appropriating value from data-driven innovation. Essentially, exnovation deals with scaling up valuable innovations through various means such as standardization, mass production, mass customization, and geographical expansion. In other words, it relates to ways of achieving market leadership, “black swan” effects, valuable disruption, first-mover advantages, and situational monopolies.

Some of the business cases presented in Chapter 6 illustrate that different combinations and chronologies lead to different outcomes. Nevertheless, we conclude that, ideally, innovation and exnovation should go hand in hand.

A book like this would normally close after Chapter 6, but our findings together with the world’s hunger for growth inspire a reflection on whether a new digital modus operandi can serve as the vehicle for such growth. Therefore, Chapter 7 takes a look at the near future and a scenario in which data-driven innovation from digital platforms paves the way for the transformation of innovation processes from analogue to digital workflows. Such a transformation may result in the advent of what we call a digital innovation economy.

The sudden occurrence of the COVID-19 crisis in 2020 and the desire to address SDG challenges also gave rise to certain reflections, which are discussed in Chapter7. These reflections relate not only to the wider repercussions of these two issues but also to the importance of accelerating services and solutions based on digital technologies and AI. Data-driven innovation may serve to mitigate the negative effects of such crises and help corporations and start-ups alike gain extra momentum.

Notes

1 OECD. (2015). Data-Driven Innova...