INNOVATION AND COMPETITION IN BIG DATA: FROM SILOS TO PLATFORMS AND MULTI-SIDED MARKETS
A silo model is perhaps the initial approach to any big data value network model, where all the processes described in section 25.2 on the bid data ecosystem - acquisition, consolidation, storage, analysis and applications - are carried out by the same entity.
This is the situation when the owner of the data is the same entity as the analyzer of the data and, more important, it is also the provider of solutions - applications - on these data.However, specialization in some of these processes or legal or strategic decisions - such as publicly funded open data, open innovation schemes - calls for a different approach where some of the above processes are externalized and performed by other players or, even more interesting, carried out with synergies and common goals between different players. In these cases, probably indications of a more mature stage in the evolution of the big data ecosystem, it would be possible to talk about a ‘platformization’ (Ballon, 2009) of big data. Data gathered about players and their roles as presented in the ecosystem section point in this direction.
In the techno-economic literature, the notion of a platform refers to a combination of technologies with product and services originated in different firms to provide a solution as complete as possible to users (e.g., Gawer and Cusumano, 2002). They are an increasingly popular strategic approach in the ICT ecosystem with examples in devices, operating systems, networks operation, services, applications and even content. Platforms are established as a means to combine the advantages of enjoying as much control as possible on the value network while taking advantage of the power of open innovation and different types of network externalities. There is an increasing number of platformbased companies that have become leaders in their domains, including the examples of Amazon, eBay, Apple, Google and Facebook.
However, the academic literature on the economics of platforms is still relatively immature and mainly devoted to strategic issues (Hidding et al., 2011). Nonetheless, it highlights relevant facts, worthy to be considered in the big data domain, especially in connection with the market positioning of the platform provider.From an economic perspective, data-based platforms can be seen as cases of multisided or simply two-sided markets. In the big data market the sides involved are users, developers of apps, providers of analytics and visualization tools, and - if the business model is linked with personalized or targeted services - marketing and advertising companies. Of these participants, users and developers are the two most prominent sides. The key stakeholder, however, is the platform owner, the provider of technology and infrastructures (servers, databases), user interfaces, development kits, business model systems and possibly some analysis and visualization tools. The owner decides on appropriate technology, sets the rules for the interaction with other players on the platform, establishes a pricing strategy, and cares about the integrity and quality of the user experience. The owner acts as a gatekeeper: controls the software and hardware evolution of the platform, encourages developers, provides a storefront for users, manages the business model, and retains (some form of) intellectual property. It is this gatekeeping position that allows the owner to extract value from the transactions taking place on the platform.
Gawer and Henderson (2007) have characterized the strategies of firms operating in the context of modular ICT systems - as has been shown is the case when describing the big data ecosystem - as the quest for ‘platform leadership’. This refers to the strategic objective to control a central system module around which other companies may develop a range of complementary technologies and products. This involves fostering a thriving ecosystem of internal and external complementary innovations and innovators; influencing architectural design through open interfaces combined with core intellectual property assets; balancing consensus and control strategies towards contributors of complementary innovations; and reflecting this in the internal organization, for example by adopting a systemic and neutral mindset that extends to the whole industry.
This results in a range of possible market-positioning schemes for platforms. Adapting the classification of Belleflamme and Peitz (2010) to the big data market, four basic strategies (not mutually incompatible) can be distinguished: resellers of data, marketplaces for data, facilitators of data analysis, and trusted third parties for data. Each of these options has specific advantages and difficulties from a technical, economic and legal perspective, an interesting avenue for further research. These strategies are also combined with the degree of openness in the platform: from closed models a la Apple, where tight control is exerted on each element of the platform, to open innovation schemes akin to open source development. In addition, platforms can range from integrated - meaning that all required hardware and software is provided through the platform - to just hardware, software or infrastructure, or even be reduced to some particular application or tool.
From a developer’s perspective the choice of the appropriate platforms is crucial as, at least in the current emerging stage of the big data market, there are a number of competing platforms and big data applications do not operate on all platforms. Choice criteria could be the size of the platform in terms of number of customers, revenues per application, or any combination of techno-economic features (amount of data, openness, data quality, availability of development tools, etc.).
25.4