“It is a capital mistake to theorize before one has data.” Sherlock Holmes, “A Study in Scarlett” (Arthur Conan Doyle)
A data revolution is changing our world: analysis, decision and actions have to be taken faster and at lower costs. The markets and market participants – whether customers, retailers, platforms, or providers of goods and services - have new needs, new constraints, new options in how they behave and make their decisions.
Products and services can be developed and released almost in real time, or co-developed with customers, requiring the use of new data analysis and interpretation methods, while still providing confidence in the relevance and quality of the results used to support these faster decisions.
Data science is at the core of this transformation. From data generation to understanding where to find data, to collection and visualization through modeling, prediction and generating relevance and meaning. With the combination of artificial intelligence techniques and large datasets, a lot of new data sources can be leveraged to better understand needs, choices, preferences and decisions.
In our research group, we are interested in analyzing and developing concepts, methods and tools to understand and help improve decision making processes. We focus, but do not limit ourselves, on at least two types of data.
In the first category are the data generated in an active way, such as questionnaires, polls, panels, laboratory experiments. In the second category are the data collected in a passive way: sensor data, user generated data, like, for example the social media ones. It can be product focused (how the user is using a product or service), customer focused (like new types of user segmentations), or market oriented (like Point-of-Sale data.).
Moreover, the very near future will show some sources of data growing exponentially and enhancing the magnifying lens we can have on our world with data, like IoT and sensor based measurements.
While self-service data, analytics tools and new ways of interacting with data are transforming the market insights industry, by focusing our research activities on new data sources and data sciences tools, we take the challenge to detect new opportunities about how data science innovations can improve, how market decisions are made in the future. This should be considered as an ongoing real-world experimentation, with some of the most relevant research questions and potential insights emerging at the intersection of real-world challenges and scientific method and rigor.