Big data refers to enormous amounts of data generated as a result of increased utilization of digital tools and information systems. There is no meaning for data unless it is contextualized, and there are four types of big data analytics. They are:
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
The advantages of data analytics include a better understanding of consumer and citizen behavioral models for maximizing commercial or policy results, proactivity and better anticipatory needs, and the ability for customizing policy solutions and reducing inefficiencies.
It also consists of the following risks:
- Increased privacy violation
- Data integration combined with cultural diversity and unsharing of data, data dispersion across organizations, countries, languages, and forms
- Enormous data processing necessitates more computing power.
The true owner of data is the source that performs the cleaning and verification of the data in such a way that it is fit for analysis. Personal data is regarded as a public good by governments and the public sector, and hence they are playing an active governing role in streamlining policy frameworks for protecting it from unauthorized processing.
As a result, the question of the way data analytics are being popularized by the government around the world and its solution is pertinent to countries such as India.
Universal model
Singapore’s Smart Nation vision considers open data sharing as a priority area, in which the ‘data.gov.sg’ portal is making data sets from more than 70 organizations public, and active utilization of data visualization can be found. More than 20,000 or 14% of public-sector officials are given training in data science. The ‘Digital Agenda 2020’ was introduced in Estonia, and it encourages the utilization of smart ICT solutions for improving quality of life and productivity.
Another important factor to be considered is the practice of government promotion and regulation of big data. Regulation is stringent in the European Union, Singapore, and Japan, where General Data Protection Regulation or a Personal Data Protection Act is in existence, and data sharing is established on deemed consent.
Meanwhile, in China, Personal Data Protection guidelines are available, which are non-binding and have a low level of regulatory rigidity. The question here is which is the most efficient and desirable one, in which the configuration of personal data protection plays a significant role.
While overly strict regulations serve as a hindrance to policymaking, public policy, healthcare research, and business, overly lax regulations will lead to invasion of data privacy, so the trade-off is important.
In this “trade-off,” data protection and promotion in Japan strike a delicate balance. While its Personal Data Protection Act forbids deemed consent concerning sensitive information, its “Next Generation Healthcare Infrastructure Act” permits the anonymization organizations to deal with medical data with deemed consent, and researchers can acquire data from the organization.
According to Japan’s Cancer Information Registration Act, hospitals are required to report anonymized cancer data to the government, which maintains a cancer database.
Data Philanthropy is also relevant in this context, and the question is, what benefits do data providers get here? Data providers resolve public issues that existing data sources cannot address, and they align business and philanthropic activities while providing benefits to customers and businesses.
Data Philanthropy reduces prospective business risks by providing a more educated policy environment, generating goodwill, supporting community partnerships, providing insights for social good, validating internal data, and sparking innovation. This is commonly referred to as the act of sharing private data for the benefit of the public.
A good example is Google Flu Trends, a data program that challenged search data for tracking influenza outbreaks. Google analyzed this search data privately but made the results public to assist health providers in tracking flu outbreaks. Similarly, Sesame Credit – Chinese Alibaba Group’s affiliate, utilizes information from Alibaba’s services for compiling a score based on social media communications and purchases made on Alibaba Group websites or paid for with its affiliate’s mobile wallet.
Sesame Credit is utilized for facilitating services such as bike share, power bank, and small loans in which benefits such as reducing the cost of trust, offering credit service to more Chinese, and assisting in the development of a trustworthy society.
Japan’s disaster big data is another notable example that aided the country in rebuilding following a tsunami/earthquake. It aided Japan in obtaining answers to questions such as:
- What was the count of the people in the tsunami impacted area at the accurate time when the tsunami struck?
- How was the behavior of people in the impacted area during the issue of evacuation warning?
- How long did it take to evacuate 100 meters down a heavily congested road?
This is based on data from car navigation GPS, tweets posted a week after the disaster, GPS data from mobile phones, disaster simulation data, government recovery policies, and so on. Data in this context reveal that not only the geographical location of the city, which is surrounded by rivers but also the heavy inflow of “pick-up” behavior, caused a severe traffic jam resulting in the non-movement of vehicles. This aided Japan in devising disaster-resistant city development schemes in the Rehabilitation Master idea.
Thus, the future activities to be taken into account by the policymakers on data analytics and confidentiality debate is that there should be transparent goals of utilizing big data, which is necessary for deploying multiple institutions. There may be a clear equivalence between the requirements of an open data platform and its operational and maintenance costs, as well as the importance of decision-making based on big data analysis.
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