Thick data is a term used to describe rich data in detail, context, and meaning. The data is procured using a qualitative approach, usually with ethnographic data, to provide a deeper understanding of complex situations and relationships. Ethnographic research is a qualitative research method that involves long-term observation of people in their natural setting, focusing on understanding how people live, interact, and make meaning of their lives. Researchers observe a particular community and collect data about people's experiences and perspectives through detailed notes, photography, and audio recordings.
Thick data is observed and gathered by social scientists and anthropologists and used to reveal people's emotions, insights, and personal views based on context and subjective reasoning. Various research methods, including focus groups, in-store videos, customer interviews, online questionnaires, and email surveys, obtain qualitative insights.
Thick data is used by organizations and businesses to understand consumer behavior and develop products and marketing initiatives that align with the emotional preferences of the target audience.
Thick data is increasingly essential in today's data-driven society, as it provides valuable insights that can be used to inform critical decisions across various industries. By leveraging thick data, organizations can better understand their customers, products, and processes, leading to more informed strategies and better outcomes.
Data scientists and social scientists work together to analyze large datasets from consumers' online behavior and datasets extracted from traditional market research methods to comprehensively understand consumer thoughts and behavior regarding cultural context, emotional reasoning, and subconscious cues.
Thick data is often understood as qualitative information that goes beyond measuring numerical output and is seen as complex and unstructured data used to understand human stories. It provides data scientists and anthropologists with valuable information that could be more easily quantifiable.
Thick data is not analyzed using SQL or Python because it needs a pre-trained structure and is often formatted as text, images, or video. Thick data needs to be processed and analyzed using special tools by data scientists, such as Natural Language Processing (NLP) image processing. NLP techniques can analyze text data, such as customer reviews or social media comments, by identifying key metrics, emotions, themes, and sentiments. Video analytics techniques can analyze interactions in a call center. At the same time, image processing techniques can be used to analyze visual data, like customer behavior in a store.
Anthropologists often use thick data to understand human culture and norms better. They may study a particular community using video footage or audio recording to understand how people communicate, interact, and behave in their environment. This information is used to identify customs and social hierarchies, which can later be used to develop products and services that best fit human desires, natural traits, and perceived realities.
The power of thick data is its potential to drive innovation. Organizations can gain a unique perspective on consumer preferences by analyzing the datasets mentioned above. This can lead to identifying new opportunities, developing innovative products and services, and implementing efficient processes.
Thick data can potentially improve customer relationships and engagement. Organizations can develop personalized marketing and sales strategies that increase customer empathy, leading to higher sales and deeper brand loyalty.
Future-use applications can also include organizations reducing risk using thick data to make better business decisions and gain competitive advantage. Businesses can gain insights into market trends, demand patterns, and other potentially troubling economic variables. These insights can reduce costs and minimize the risk of making large-scale mistakes.
Thick data is present in healthcare, where patient records contain large amounts of detailed and contextual information, including medical notes, lab results, and prescriptions. This rich dataset can be used to gain insights into patient health and well-being and identify trends and patterns that inform clinical decision-making.
Thick data in healthcare might include a patient's genome sequence, which can be analyzed to gain insights into their risk of developing certain diseases. Thick data can include information such as electronic health records, social media feeds, and sensor data, which has the potential to provide a more comprehensive view of a patient's health and daily habits.