Why is Data Science important in cultural field?
Nowadays, we are living in a world made of data. Data is everywhere: it is behind the strategies of a supermarket chain, the decisions of a big company, the study of a scientific research or the organization of an event. Thanks to data science, it is possible to easily manage scientific innovations, algorithms and complex methods to extract knowledge and information that can be useful to a great range of application domains. Among the various fields to mention, also the cultural one is included. Indeed, the benefits of data analysis, data mining and machine learning studies in the management of cultural organizations are clearly effective. But why and how are they so important?Let’s imagine a very simple situation: the coordination of a cultural event. Events attract people (depending on their scope and resonance) and people bring money to the event’s organization, as well as to many other activities that operate in the area, like restaurants, hotels or transports. To figure out an adequate way to measure the economic investment and to guarantee great participation, it is necessary to know the number of people that are going to take part in the event, their movements, behavior, interests and characteristics. In simple terms, we need knowledge.
Considering that cultural organizations need to estimate their social and economic impact to have a more active role in society, it is therefore evident why it is so relevant to solve these issues. In this kind of situation, data analytics approaches can help us through different strategies and can be extremely useful to extract indicators measuring the impact of cultural events on the area that hosts them, such as the consequences on the economy, the involvement of different segments of the population or the growth of tourism. In order to derive them, data understanding is the first step that every data scientist should undertake for a better framing of the data which is available. For instance, it is important to study the public and its peculiarities: is it a younger or an older public? What sort of habits, interests or occupation does it have? In this way, it can be quite simple to get an overview of the target that should be considered.
Even though a lot of people would not notice the difference between extracting information through Data Science algorithms and making simple projections with Data Warehouse instruments, actually there are many. First of all, using Data Science’s techniques can handle much larger volumes of data, which is a very important requirement for analyzing the big data we are all surrounded by. On the other hand, there are also some advantages in using software programs such as Excel, and one of them is naturally the fact that they are pretty simple tools that can be easily understood by anyone, whilst Data Science methods tend to be more complex to learn. But the improvements that the latter can bring are much more significant: it is not only about making simple statistics on data, indeed Data Science can help to study different kinds of data in much more interesting and thorough ways, such as text processing or speech recognition, convenient for tasks like Sentimental Analysis.
Other helpful operations are the different types of analysis for discovering patterns among the data. One of them is the Clustering Analysis, which is a method used to group data together and that can be exploited to see, for instance, how many groups of people attend the events and in which area they tend to move. Other useful methods can be those related to Classification, which can be employed for classifying data and generalizing known structures, for example, it is used to classify emails as spam or non-spam. In addition to that, there is also the Association Rules Mining, which helps to look for relationships among features of large data sets. All of these data analysis approaches can lead to many disparate studies: for instance, we can discover the most crossed paths by the audience, the most frequented days of the events during the week or the timetables during the day or even what kind of public participates.
One of the most important tasks on known data is the prediction of new data: it is certainly a very useful statistical operation that can be studied with the purpose of anticipating the public’s behavior during an event so that it can be organized and structured with foresight and awareness. As it is clear, data science can offer to the world of cultural events a new way of thinking, understanding and studying different points of view, through which it will be possible to make people’s experiences increasingly enjoyable.
