Open Data: Trends, Benefits, and Implementation Challenges Introduction

Technology has revolutionized the way things are done. Today, thanks to advances in technology, information is being shared and distributed in ways that appeared impossible a few decades ago. With the Internet of Things (IoT) promising to further revolutionalize the availability and distribution of information, it would be fairly accurate to state that we are likely to witness greater possibilities on this front going forward. This text concerns itself with the trends in open data. In so doing, it will also highlight the benefits of open data as well as the implementation challenges that continue to be witnessed in various jurisdictions. The discussion will be founded on lessons and examples from India.

Discussion

It is important to note, from the onset, that there is no single assigned definition of open data. This is to say that various definitions have been presented in an attempt to define open data. One of the most simplistic definitions to open data has been offered by the Queensland Government Chief Information Office (QGCIO). In this case, open data is defined as “data that anyone can access, use or share” (QGCIO, 2018). Towards this end, open data ought to not only be available, but also have no restrictions with regard to redistribution and reuse. It should be noted that with regard to global open data trends, the advent of the internet has effectively promoted access to data. As the digital revolution continues to steam ahead, open data is likely to play a prominent role in the way persons engage, learn, and relate. Open government data (OGD) in most developed countries also continues to gather greater support. This is more so the case given that on this front, open data is deemed to be a political and socioeconomic tool to promote accountability. However, in countries where democratic space continues to be constricted, open data is yet to gain the favor of political formations. In the words of Schwegmann (2012), “the number of open data initiatives in developing countries is still limited, but the coming years will probably see a large increase of open data initiatives in developing countries.” In India, the number of open data enthusiasts has experienced exponential growth in recent years. One such example, according to Parihar (2015), is DataMeet. In basic terms, DataMeet is made up of enthusiasts of data science. Its engagements usually revolve around the collection, arrangement, as well as utilization of open data.

Benefits of Open Data

The relevance of open data cannot be overstated in the world in which we live in. To researchers, open data has significant and undeniable benefits. In addition to the further enhancement...

It is also important to note that open data also grants researchers greater efficiency in the analysis as well as sharing of data, and also helps in the minimization of academic fraud via the further enhancement of transparency (Emory University, 2018). Researchers in India have, however, been slow to fully appreciate the benefits of open data as far as far as the advancement of their work is concerned. This is despite the launch of an open access policy by DST and DBT that sought to ensure that research was more accessible. Gutam (2017) points out that “Indian researchers have deposited an abysmally low number of papers in these repositories.”
The benefits of open data further extend to the medical field. According to Pisani et al. (2016), a few years ago, the open sharing of medical data was frowned upon by those in the health care industry due to fears that personal patient data could be accidentally shared. However, as the authors now observe, “data sharing is being herded into the mainstream by pioneering researchers, with added pressure from funders, medicine regulatory authorities, public health agencies, and medical journals” (Pisani et al., 2016). According to the authors, this is more so the case given that data sharing in the health sector has been associated with improved patient outcomes. The privacy concerns raised in this case continue to be addressed via the adoption of a variety of measures. Some of the data sharing examples that have been listed by Pisani et al. (2016) include, but they are not limited to, the Yale University Open Data Access and the WorldWide Antimalarial Resistance Network.

Implementation Challenges

Infrastructure remains one of the most veritable challenges to open data. According to Mulvany (2016), field-wide data infrastructural support is often lacking as a consequence of the explosion of data in some fields. This is also a concern in India. As Parihar (2015) points out, India’s “infrastructure to support efficient data collection, processing and management needs to be strengthened.” Next, there are also concerns that relate to the anonymisation of data. According to Green, Cunningham, Ekblaw, Kominers, Linzer, and Crawford (2017), raw data in some instances contains personally sensitive information. In the words of the authors, the fact that databases are not immune to manipulation means that “information that might not be deemed PII can lead to the identification of a specific individual and enable inferences to be made about that individual” (Green et al., 2017). For some,…