One of the most sought commodities today is data. The digitalization has given rise to a technical revolution that could be of the same magnitude as that by electricity. Data and the information it carries has great value for both commercial and non-commercial use cases and the chase for our personal data has just started. Companies realize that their customers want to have more personalized products and services, and in order to satisfy their customers' needs, they need to collect as much data as possible to understand the profiles of their customers. Once the data is collected, Artificial Intelligence (AI) can be used to understand and construct customer profiles that reveal the needs of each individual customer. Thus, data has become the new oil, the fuel that empowers AI, and especially Machine Learning. Machine Learning is a subfield that constructs artificial intelligence based on data as input to train a machine to perform certain tasks. This is opposed to a rule-based approach, where machines are programmed based on pre-defined rules by humans.
Much of the data collected and used today is to understand human behavior, which is very important from both a societal as well as a business perspective. Everyday, we interact and observe each other's decisions and body language to collect enough data to understand people around us. These impressions that we both make and get form the basis for some sort of classification of different people around us. Based on this classification, we act and react in a certain way around different profiles of people. In a similar manner, the data of our "digital selves" and our interactions and activities using our digital devices reveal interesting properties of our profiles.
So how can this kind of insight of different profiles be useful when profile data is available in a digital format? Recommender systems, or expert systems, are actually examples of how data is used to build profiles and act based on it. Recommender systems consist of two main parts. The first part is classification and segmentation of people, markets, products, and services. For instance, people can be partitioned based on different attributes such as age, gender, geographical region, language, martial status, etc. Also, our digital traces such as our web-surfing activities may also contribute to the classification task of different user or customer profiles. But the attributes we take into account should be relevant to the market, product, and/or the service in question. Based on this insight of different profiles and segmentations, a recommender system can build a model to be able to predict the behavior of users and customers.
Companies like Amazon, Netflix, and Spotify have recommender systems that are based on this model. Amazon has profiles based on both people's personal attributes as well as their shopping history. People with similar profiles and shopping histories get similar recommendations. Netflix has rating systems for shows, series, and movies. For instance, if Alice and Bob liked similar movies, a movie that Alice liked will be recommended to Bob. Spotify's "Discover Weekly" functionality works in a similar manner. Depending on the listener's taste (music consumption history), the recommender system would suggest relevant music content.
Another practical example is Barack Obama's campaign when running for president. For the first time in American history, a presidential election campaign was utilizing AI. Obama's campaign partitioned the voters in three groups. Those who are loyal to the Republican Party, those who are loyal to Obama's Democratic Party, and those in the grey zone that hadn't decided yet. No resources were wasted in calling the loyal ones for either party. Instead, the campaign targeted calling and visiting people that had not decided yet. Also, the call and visiting days and hours were chosen to maximize the reach rate. The timing was based on a careful study of the voters' profiles. This strategy played a vital role in Obama's election for president in the United States.
Data will have a tremendous impact in our society, from commercial to societal and political. There is no doubt that the value of data and the information it carries will grow ever more important. One should not be surprised that data will be so valuable that it might become one of the most expensive commodities, and perhaps a currency to trade with in the future.