In the realm of data-driven insights, one data category stands apart in its complexity and potential for transforming businesses and industries - human observation data. Unlike standardized financial or manufacturing data, human observation data captures the intricate fabric of human behavior, physiology, and context. As we navigate the shift from traditional data paradigms to advanced data platforms, it's crucial to understand the distinct characteristics of this real-world data (RWD) and how they influence storage, analysis, and the capabilities of modern data platforms to enable human-centric businesses to optimize their most important asset, their people.
This type of data is often qualitative or semi-quantitative. It is typically gathered by individuals, researchers, or teams who directly observe and record what they measure, see, hear, or otherwise perceive about human subjects. Objectively tracked human observation data is becoming more prevalent, with smartphones, wearables, and other sensor-embedded devices allowing for the collection of data related to various physiological and behavioral parameters. Human observation data can encompass a wide range of contexts, from healthcare and psychology to social sciences, sports science, and more.
Some examples of human observation data sources include:
Such a dataset, comprising objective and subjective observations of employees' behaviors, characteristics, activities, and well-being, can provide comprehensive insights that drive informed decisions and tailored strategies. This data can not only be an asset for your teams and overarching organization but can offer a clear competitive advantage in your industry, optimizing for a data-driven future while improving efficiency and outcomes.
Some examples of the value of this data and analysis are:
Unlike simple structured datasets found in more historically leveraged "big data" datasets, human observation data is rich in contextual nuances, reflecting the intricate interplay between personal factors and environmental influences. This uniqueness requires specialized data storage and analysis approaches, blending qualitative and quantitative methods, to uncover insights into human behavior, health, and performance that traditional data warehouses and platforms are not optimized for.
Some key examples of the nuance and complexity of human observation data are:
Human observation data often includes time-related aspects, showing how things vary or change unpredictably over time. This is especially true in wearable data, where measurements happen continuously or at set intervals. This dynamic nature captures how behavior and body indicators change over time and needs special methods to store and analyze sensor data.
Unlike financial and manufacturing data that often follow general patterns, human observational data is very individual. It reflects each person's unique movements, habits, and responses. It also covers various groups of people, so we need methods that consider individual differences while spotting common trends. When improving an individual's well-being, we often focus on their unique needs, not general trends.
Keeping track of data provenance, which includes where data comes from, how it changes, and how it's used, is crucial to ensure data's reliability and trustworthiness. This helps us confirm that the data is accurate and reliable, which is essential for data quality, following rules, and making good decisions. It also helps improve data for future use and minimizes bias, making it ready for artificial intelligence (AI).
The richness of human observation data calls for a wealth of metadata. This context-enhancing information, such as environmental conditions, personal experiences, and contextual factors, enriches the analysis by providing deeper insights. This metadata requires storage capabilities optimized to track relationships between complex layers of sensor data, sampled data, and contextual data.
Human observation data can come from diverse sources, including wearable sensors, video recordings, physiological measurements, behavioral observations, and self-reported assessments. Unlike most data that has thus far capitalized on big data analytics, human observation data often involves subjective experiences and qualitative information that cannot be easily quantified, requiring further considerations for analysis.
This multimodal nature captures a holistic view of an individual's experiences and behaviors and often benefits from interdisciplinary collaboration. The analysis and interpretation of human observation data must leverage approaches outside of engineering or finance. For example, expertise and considerations from the social sciences, clinical research, behavioral psychology, sports science, biomechanics, and epidemiology are necessary to transform this data into actionable intelligence.
Human observation data is sensitive, including behaviors, health, locations, and emotions. It needs strong privacy measures, such as encryption and access controls, to avoid misuse. Ethical concerns, consent, and data ownership make specific privacy and security rules and infrastructure crucial to protect confidentiality and individual data storage and analysis rights.
It is time for organizations and teams to leverage innovation in big data analysis for more human-centric optimization, but this will require specific considerations, techniques, and intentionally developed systems: