The macro question that drove the first part of the course is: how is it possible to offer free digital services? And the answer is directly related to orthogonal two-sided platforms. Traditionally, business models based on a free offer towards the end users were based on the Client-as-a-target logic, therefore with the involvement of advertisers as orthogonal actors, as a second side. We've seen how digital technologies, smartphones, and the various smart sensors we increasingly use in our daily lives enable a second strategy, called Client-as-a-source, which can be declined into three different strategies: Enhanced Advertising, as in the case of Facebook using data to better target advertising, the second, E-Ethnography, as in the case of Nike+, which offers data to the parent company to study user behavior with its products and gain insights that allow it, for example, to develop new products, and third, Data Trading, as in the case of Strava and Strava Metro, which involves the actual sale or exchange of data with third-party players who see clear value in that data. In all cases we're looking at strategies that bring a platform to life, starting with a typically one-sided digital service, looking for sustainability in the business model. In other words, services such as Nike+ or Strava are born as pure digital services, with only one side - the end users. In the search for a sustainable business model, various actors have been identified, internal actors, in the case of Nike, external ones in the case of Strava, who see value in the data collected during the provision of the service and can help the achievement of economic sustainability of the overall service. This possibility leads to the identification of a second side and therefore to the birth of a two-sided orthogonal platform, in other words, this kind of platforms are such for deliberate choices at the level of business model, a bit like newspapers, which choose to become a two-sided platforms bringing on board advertising investors. It is interesting to see how this type of platform does not suffer, precisely for this reason, from the chicken and egg paradox that characterizes other types of two-sided platforms. Changing the perspective, we can see these platforms as something that was created by leveraging the value of something created for a completely different purpose. The data in all these cases are generated, collected and analyzed with a clear purpose: service delivery to the end users. The fact that it is then available to the service provider, the platform, is in some way a side effect. In other words, this data is basically waste from digital service delivery, a by-product. The ability to identify an intrinsic value in this data, which can be recognized by other actors to the point where they are willing to pay for it, take the name of "data-driven epiphanies." In most of the cases we've named, there's no planning behind them, but the platforms realized the value they had in their hands when they already had the data in hand. We're talking about cases that are temporally located in the years around 2010, when these opportunities were literally emerging in the digital world. These epiphanies have some implications that we need to consider. First of all, data have value only if they are collected in substantial quantities, possibly with some heterogeneity in the variables involved (in the case of Strava it means having a service spread across many cities for example) in order to actually be offered to someone or more simply to see the value in this data. This explains at least in part the epiphanies that recur in many data-driven stories: in order to understand who the data might be useful to...it's useful to already have the data. Second, we need to identify the peculiarities of our data, so that we can understand who might be interested in it. Think for example of the cases of Strava and Nike, but also of Waze and Google Maps. All of these apps collect data on movements within cities, but they have implemented different data exploitation strategies, partially because they have different data even though they refer to the same macro type. For example, Strava can also be used by runners, but it is very popular among cyclists, making its data unique and particularly interesting for the transportation departments of various cities. Another point to consider is the identification of orthogonal actors. Unlike transactional platforms, where the type of actors is somehow defined in the design of the platform, here we are faced with a more exquisitely business logic, in which the platform offers a package of data to potential customers....who will have to be coaxed into buying the service. This dimension also pushes the epiphany dimension of these businesses, having the data in your hands helps you understand who might be interested in it. A final element to consider is the process for facilitating this epiphany. Looking at the data we might often come across fairly straightforward assumptions, for example other actors who are typically part of the value chain of that data, such as bike manufacturers in the case of Strava. But here we have to ask ourselves questions: is this data really useful for them? What could they learn from this data? And more importantly....don't they already have similar data available to them? Why would they want ours? Often in answering these questions we find that the most obvious actors already have data similar to ours or even collect it themselves, often the most interesting answer is not so obvious...like transportation departments for Strava. Hence the idea of the epiphany. Although, without waiting for ideas to come from nowhere, in the last lesson of this course, reviewing the Platforms Thinking process, we will introduce the Data Board, a tool aimed at stimulating and facilitating this kind of epiphanies.
