Data Science in the Living Environment: Revolutionary, Redundant or Risky

Nikita Klimenko
Final Class Assignment, S.6963 Spring 2023

The emergence of smart home assistants, sleep cycle sensors and other data analytics tools in indoor space has spurred the interest of real estate developers, homebuilders, insurance companies, architects and technology firms looking to benefit from new sources of valuable human data and subsequent insights into better design and operations of indoor spaces. This has also raised potential concerns about privacy, data security, and new power dynamics between the stakeholders engaged in the operation of indoor environments. In this paper, we summarize major application frontiers related to data science in the living environment, outline key risks and opportunities and evaluate the most promising use cases within established analysis frameworks. We argue that the many proposed use cases are unlikely to scale soon due to poor data interpretability and lack of clear objectives; however, major opportunities exist in the insurance and health monitoring sectors.

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Disclaimer:

This paper was written for Alfred Spector’s MIT Spring 2023 course 6.S963 Beyond Models – Applying Data Science/AI Effectively. It has not been peer reviewed, and its content does not necessarily reflect the instructor or the authors’ views.