On the Concept of Innovation

According to the Merriam-Webster dictionary, the term innovation is defined “as the introduction of something new” or “a new idea, method, or device” [1]. At a closer look, innovations can be incremental or radical [2]. Incremental innovations are stepwise improvements within an existing technological approach, e.g. the evolution from a manual light switch in your car to an automatic sensor-controlled switch that turns on the light when it gets dark to a smart light that automatically adapts to actual driving conditions. Radical innovations, in contrast, are entirely new concepts or novel ways to think about a product, e.g. the introduction of a digital camera that changed an entire market. Incremental innovations come with less risk as customers are used to the product and it is easier to sell. Radical innovations have higher risk—they could easily flop—but also hold higher potential since first-mover advantage gives them a long-term competitive edge.

Another distinction looks at the impact of innovations. There are sustaining and disruptive innovations. Sustaining innovations improve the performance of established products (e.g. the evolution from VHS to DVD), while disruptive innovations contain a new business model or unique value proposition (e.g. music streaming services compared to traditional music stores). [3]

How can Analytics Support Innovation?

People rarely wake up in the morning with a brilliant idea and present a market-ready product by evening. In reality, the innovation process contains a lot of work and research, in which the actual idea is only a small (but important) part. Throughout the process, data is critical for learning about the needs of the market, product bugs and issues, competitive solutions, and many other things. As such, analytics plays an important role in the innovation process.

Figure 1. Analytics Methods in the innovation process

Figure 1 shows a framework that illustrates how analytics can support the innovation process. The framework plots the type of innovation (radical and incremental) against the source of the innovation (internal and external). These two dimensions build four sectors that can be used to discuss the role of different analytical methods.

Internal-Incremental: Management Reporting & Dashboards

Using internal data for incremental advancements is probably the most obvious way of innovating. Traditional management reporting and dashboards serve this purpose by quantifying business performance and trends. For instance, if sales data show that customers increasingly buy small cars, the auto manufacturer needs to make more small vehicles.

External-Incremental: External Data and Data Marketplaces

External data generates new perspectives about the market and the company’s performance in it. For example, external market data might indicate that certain customer segments have an affinity for environmental concerns and thus would respond well to hybrid or low-emission vehicles and other “green” innovations.

Radical-Internal: Data-mining & Simulation, Predictive Analytics and Analytics-as-a-Product

In rapidly changing markets, radical innovations are almost mainstream. Here, techniques, such as data-mining, simulation, and predictive models can reveal hidden patterns that companies can use to drive radical innovation. Moreover, data and analytics can not only help to identify innovations, but also be the innovation itself, e.g. by providing new data-driven business models that change the value proposition. For instance, a machine manufacturer collects and analyzes sensor data so it can sell new predictive maintenance services.

Radical-External: Provision of Open Data and APIs 

Radical innovations that come from external parties (e.g. partners or the “crowd”) can reduce the risk of failure, as the external effort usually arise from a certain need. Moreover, external parties often have different perspectives that make it more likely they will come up with radical new ways of thinking about or doing things.

Open data and public APIs are helping to drive this type of innovation. [4]. For instance, the Schiphol Airport in Amsterdam provides various public APIs [5] that people from all over the world can access for free to generate new ideas and applications. Many companies have followed this concept and now share their data and APIs with business partners to build a service ecosystem around their products and thereby increase its value. For instance, a smart product like an internet-enabled light bulb becomes more valuable, if it is used by other smart products, such as a voice-assistant.

Conclusion: Sharing data in Data Ecosystems Can Pay Off

The article shows that data and analytics is essential for innovation and thereby a very important asset for organizations to compete in the market. Accordingly, organizations should reserve a prominent place for data and analytics in their innovation strategies. Second, novel analytical methods, such as predictive analytics and simulation, can surface patterns that drive valuable innovations. Last, many organizations can benefit from external data to drive innovation. They can also reap rewards by sharing data with partners and the public via open APIs that create vibrant data ecosystems, which many experts predict are the future of data-driven business models.

The next article in this series will dive into the concept of data ecosystems and discuss reated patterns, challenges and value propositions.

Further reading

[1] https://www.merriam-webster.com/dictionary/innovation

[2] https://bmilab.com/blog/2017/8/3/two-strategic-approaches-to-innovation-incremental-vs-radical

[3] Clayton M. Christensen., “The Innovator’s Dilemma”, 1997

[4] Borgognoabc, Oscar; Colangelodef, Giuseppe, “Data sharing and interoperability: Fostering innovation and competition through APIs” in Computer Law & Security Review (35, 5) 2019

[5] https://www.schiphol.nl/en/developer-center/page/explore-all-schiphols-apis-in-the-developer-center/