There’s a lot of buzz around the term “human data science.” What is it, and why should you care? Human data science is a relatively new field that combines the study of humans with the techniques of data science. By understanding human behavior and using big data techniques, unique and actionable insights can be obtained that weren’t possible before. In this blog post, we’ll discuss what human data science is and give some examples of how it’s being used today.
What is human data science?
Human data science is the study of humans using data science techniques. It’s a relatively new field that is growing rapidly as we learn more about how to use data to understand human behavior. Human data science combines three areas of study that have traditionally been separate: human data, human science and data science. Human science is the study of humans, including our behavior, thoughts, and emotions. Data science is the study of extracting insights from data. Human data science integrates life sciences with breakthroughs in data sciences and technologies to understand human behavior at a scale and level of detail that wasn’t possible before.
Some examples of how human data science is being used today include:
- Using social media data to understand human behavior
- Studying retail data to understand consumer behavior
- Analyzing mobile phone data to understand how people move about cities
- Using DNA sequencing to understand disease risk factors
The following are some of the benefits of human data science:
- Optimize health systems performance by guiding decision-making with data: By understanding human behavior, we can optimize health systems to be more effective and efficient. For example, human data science can be used to understand how people make decisions about their health. This information can then be used to design better policies and programs that encourage healthy behaviors.
- Disease prevention and treatment: Human data science is also being used to map the spread of diseases. By understanding how diseases spread through populations, we can develop better strategies for prevention and treatment. Human data science is being used to speed up the clinical research process. By understanding how humans respond to new treatments, we can get lifesaving therapies to patients faster.
- Delivery of human health services: Human data science can help us understand how people use and interact with health services. This information can be used to design better service delivery models that meet the needs of patients and improve outcomes.
Challenges for human data science
The following are some of the challenges which need to be tackled for making desired progress in the areas of human data science:
- Data collection: Data collection is a crucial part of human data science. In order to understand human behavior, data must be collected from a variety of sources. This can include surveys, interviews, social media data, wearable devices, and more. The challenge is to collect data in a way that is ethical and respectful of people’s privacy.
- Data integration: Once data has been collected, it must be integrated. This can be a challenge because data from different sources can be in different formats and may not be compatible with each other. This is where a set of standard data structures is needed to represent the human data. Data integration is the process of combining data from multiple sources into a single dataset
- Data privacy: Data privacy is a major concern when it comes to human data. In order to get accurate results, data scientists need access to large amounts of data. However, people are becoming more and more aware of the ways their data can be used and are often reluctant to share it. This is a major challenge that needs to be addressed in order for human data science to continue to grow.
- Data quality: Another challenge is data quality. Human data is often messy and unstructured, which makes it difficult to work with. Data scientists need to be able to clean and wrangle data in order to get accurate results.
- Data bias: When collecting data for building AI / machine learning models, data bias can throw off the results of an analysis. The models will often become biased because it relies on historical data which may be inaccurate or incomplete. This is a challenge that needs to be addressed in order to get accurate results from human data science.
- Gaps in understanding of diseases: Another challenge for human data science is that there are still many gaps in our understanding of diseases. In order to find new treatments and cures, data scientists need to be able to work with healthcare professionals to collect data on patients. The challenge working with healthcare professionals is that they are often reluctant to share data due to privacy concerns.
- Lack of human expertise to train the AI application: Human data science often relies on artificial intelligence (AI) to analyze data. However, AI applications need to be trained by humans in order to be effective. This is a challenge because there is a lack of dedicated human experts who can help train AI applications.
- Lack of supportive policies and regulations: Another challenge for human data science is that there are often no supportive policies or regulations in place. This can make it difficult to collect data and share results. It also makes it difficult to ensure that data is used ethically and responsibly.
- Lack of investment in research & development of diseases discovery: Human data science is a relatively new field and there is often lack of investment in research and development. This can make it difficult to find new treatments and cures for diseases.
Human data science is a rapidly growing field with endless potential. By understanding human behavior, we can make better decisions, create more efficient systems, and improve the world around us. There are challenges that need to be addressed in order for human data science to reach its full potential, but with the right investment and dedication, anything is possible. Stay tuned for more blog posts on this exciting topic! As always, if you have any questions or would like to learn more about human data science, please contact us.