Big Data and The Lean Startup Approach as Tools for Innovation in Large Firms

Big Data and The Lean Startup Approach  as Tools for Innovation in Large Firms

Can larger firms face and survive the challenge of startups? The one question that comes to mind these days is whether they are still capable of fostering innovation. In a recent manuscript published in AMS Review, ESSEC Professor of Marketing Steven Seggie and his fellow researchers, Emre Soyer, and Koen Pauwels, examine how incumbent firms can adapt their business models to allow for innovation that will enable them to compete and survive in the face of greater competition from both local and global startups. Many large companies try to adapt to this new challenging environment by behaving like startups, which, as the researchers point out, is not the key to successful innovation for incumbent firms.

Adapt or… Die Trying

Previous research shows that incumbent firms find it difficult to adapt their business models (and thus their strategy) for various reasons including the complexity of the organization, a focus on short-term rather than long-term gains, and competition for resources among managers. Large companies often suffer from innovation blindness caused by the very fact that they hold onto outdated models and assumptions on how the world works. This difficulty in changing the business model makes it extremely challenging for firms to respond to the new forms of competition brought forth by startups. While changing the business model is often necessary, if not vital, there are no clear best practices and many firms have followed the route of trying to behave like a startup. This approach, however, is doomed to fail as it does not recognize the fundamental differences between the two types of organizations in areas such as resources, speed of decision-making, focus etc.

Adapt. Do not adopt!

There has been research encouraging large companies to adopt the lean startup methodology[1] for product innovation, suggesting that in this way, legacy companies would be able to quickly adjust and adapt the business model to create and appropriate the most value. But while a startup is by definition “an organization formed to search for a repeatable and scalable business model”, a legacy firm already has a business model. Therefore, to be economically competitive, incumbent firms need to be ambidextrous. In other words, they should be able to execute in present markets while innovating for new ones. According to Steven Seggie and his peers, incumbent firms should leverage advantages such as big data and adapt (not adopt) the lean startup methodology. Let us not forget that big firms have clear advantages in big data both through the amount that is available to them and also through the resources they have to analyze the data and act upon the results of the analysis.

It is not the Size of Your Data that Matters but What You Do with it

The real question then is: “How should firms leverage big data and adapt the lean startup methodology as a means of changing the business model to allow for successful innovation and successful competition with startups?”

Traditionally, big data analysts have talked about the 3Vs of big data: volume, variety, and velocity. Each of these characteristics creates a learning challenge, which can then be addressed through use of parts of the lean startup methodology.

Volume
Volume refers to the increasing amount of data that is available. This volume leads to confirmation bias as a greater amount of data provides opportunities to confirm prior beliefs that inform decision-making. The solution provided by the lean startup methodology is to use the analysis of big data not to reach conclusions but instead to develop hypotheses, which can subsequently be tested through experimentation.

Variety
Variety means that firms have access to data from very different sources that were not available in the past. Although variety is seen as a good thing, it leads to an increased complexity of both the data and analysis, thus making it difficult to communicate insights for decision-making. The lean startup methodology suggests the introduction of a concept called innovation accounting[2]. It requires regular reporting on the progress of an innovation project with a decision to quit, persevere with, or pivot. The advantage is that it facilitates the access to insights throughout the process.

Velocity
Velocity refers to the fact that firms are getting real-time data. The richness and timeliness of the data suggest an increased ability to predict the future, and thus creates an illusion of control. The solution offered by the lean startup methodology is to include a build-measure-learn loop into the innovation process as this allows firms to engage in validated learning on an incremental basis. The risk is minimized, as all innovations are incremental in nature. So even if managers have the illusion of control, they will not be able to take large risks that may come back to haunt them in case of unexpected occurrences.

Let Us Call a Spade a Spade

With unprecedented amounts of Venture Capital money being invested in startups, incumbent firms are under greater pressure than ever before to maintain their status as leaders in their fields. Some of them have adopted, recklessly, the lean startup methodology with generally disastrous results. In sum, a startup is not a small version of a legacy company, neither is a legacy company just a large version of a startup. Therefore, incumbent firms should adapt the lean startup methodology instead of adopting it as it is. Firms should leverage the resource advantages they have regarding big data and combine these advantages with the adapted lean startup methodology to enable large companies to adjust their business models to allow for successful innovation.


[1] The lean startup methodology is a quick and iterative process that requires minimal resources compared to more traditional models of innovation (Blank, S. (2013). Why the lean startup changes everything. Harvard Business Review May, 4–9.)
[2] A measurement process used to evaluate innovation throughout the innovation process

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