What is Big Data?
Boyd and Crawford (2012) define Big Data as “a cultural, technological, and scholarly phenomenon that rests on the interplay of: 1) Technology: Maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets; 2) Analysis: Drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims; and 3) Mythology: The widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy”.¹
However, Big Data is not simply defined by the large volume of its datasets. Currently, other characteristics (V’s) attributed to Big Data can be found in the literature, including:²
• Variability: Lack of structure, consistency, and context
• Variety: Includes audio files, imagery, numerical data, and text data
• Velocity: Real-time processing and very high speed of transmission
• Veracity: Accuracy, noise, and uncertainty in data
• Volume: Extremely large data sets

In addition, two other V’s are also mentioned:³
• Validity: Data correctness and accuracy for the intended use
• Volatility: How long are the data valid and how long are they stored? At what point is data obsolete to current needs?

How can Big Data be used in health?
Several studies have illustrated the potential of Big Data in diverse health fields including epidemiology, precision medicine, and public health.
For instance, of the timely tracking of emerging epidemics can be achieved via online queries on disease symptoms posted on social media platforms like Facebook and Twitter. In clinical trials for precision medicine, large-scale population studies and Big Data analytics can lead to more precise drug development and more accurate treatments.²
In this regard, Hansen et al. (2014) broadly identify three Big Data streams in health:⁴
• Traditional medical data primarily originating from health systems (e.g., EMRs, personal and family health history, medication history, lab reports, pathology results).
• “Omics” data from large-scale datasets in the biological and molecular fields (e.g., genomics, microbiomics, proteomics, and metabolomics).
• Data from social media (e.g., Twitter, Facebook, etc.) and quantified self-movement (e.g., FitBit, mobile applications, wearable devices, etc.).

What are the ethics issues related to Big Data?
The current literature points to several ethics issues in the context of Big Data:
• Privacy: In the era of Big Data, the ability to protect individual privacy is limited. This creates potentially important consequences for privacy rules in scientific research.⁵ As such, the potential benefits and progress of Big Data “cannot be ensured unless the cultural and ethical issues related to patient privacy and the need for individual institutions to maintain some degree of control over the data they collect are addressed.”⁶
Moreover, while privacy protection is a right, the risk of data security breaches may be increased in settings where legislation is underdeveloped, which is of particular concern for vulnerable populations and communities.⁷
• Consent: Traditionally, consent is taken for participation in a single study, while Big Data aims to reveal unforeseen connections among many data points, involving multiple uses of individuals’ data over time.⁸ Consequently, researchers are exploring novel consent such as “broad” and “blanket” consent.
• Transparency and Trustworthiness: Richards and King (2013) argue that Big Data creates a “Transparency Paradox” because while it promises to make the world more transparent, “its collection is invisible, and its tools and techniques are opaque.”⁹ People have the right to know how their data is being used; therefore, transparency and accountability in Big Data must be enhanced.
The commercial use of health data is particularly concerning, due to risks of data misuse, profiteering, and commercialization. Hence, trustworthiness and transparency of processes are key.¹⁰
• Deepening “Digital Divides” and Global Power Imbalances: Reaching the full potential of Big Data depends on proper infrastructure and human resource capacities to store, process, analyze, and exchange data.
However, because IT infrastructure requires a significant investment of time and money, ensuring these capacities can be challenging in resource-constrained settings.¹¹ Most of the necessary hardware for Big Data is located in high-income countries “and access to information and resources is skewed by a very unequal distribution of telecommunication capabilities to access them.”¹²

Richards and King (2013) argue that Big Data generates a “Power Paradox” that will create winners and losers, likely benefiting “institutions who wield its tool over the individuals being mined, analyzed, and sorted”.⁹ Moreover, there are ethical questions related to ensuring data accuracy, limiting bias, and population benefiting from Big Data analytics. For example, findings derived from social media may not be representative due to limited access by minority groups² or by digital and data divides between urban and rural areas.¹³

The Big Data phenomenon continues to take place in an environment of uncertainty and rapid change. Although there are various studies underway to analyze the potential benefits and costs of Big Data, critical questions need to be further explored—in particular, we need to know “what all this data means, who gets access to what data, how data analysis is deployed, and to what ends.”¹