Digital Data Assets can solve these Global Problems
World hunger
More than a billion people, or 12 per cent of the world’s population, are suffering from hunger. After a decades of global hunger levels, the United Nations announced recently that the number of people going to bed hungry is steadily rising. Despite enough food being produced every year to feed all people on the planet and more, a small but substantial amount of this is wasted, even in developing countries.
From using agricultural devices connected to the IoT to gain insight into crop health in a bid to improve yield quality, to reducing the quantity of lost and damaged foodstuffs by implementing IoT monitoring devices in the entire distribution ecosystem, IoT solutions can offer digital data-driven insights and play a role in eliminating world hunger.
According to business information provider IHS Markit, the total number of IoT devices is forecast to reach 125 billion in 2030, up from 27 billion in 2017, creating an IoT network with unprecedented coverage levels. Sharing real-time data about urgent food requirements and wastage will make the distribution of limited food supplies as effective as possible. Digital data assets recording these stats on the blockchain is pivotal to eliminating the global crisis.
Modern slavery
Modern slavery and forced labour is major problems found to exist in more than 165 countries across both the developing and developed world. The Modern Slavery Act was introduced by the UK government in 2015 to combat this global injustice, but charity Focus on Labour Exploitation has called on companies to do far more on this issue.
Due to poor procurement practices and unaccountable supply partners, some companies have unknowingly participated in unethical supply chains and, in the process, are exacerbating issues surrounding modern slavery. Smart procurement technology greatly improves the ability of major companies to achieve a comprehensive view of often complex supply chains and root out irresponsible suppliers.
By efficiently capturing information from suppliers and complementing it with third-party data, organisations can better evaluate suppliers and ensure their supply chains don’t support practices such as slavery and child labour,
Moving away from simplistic supply chain checks will damage the widespread modern slavery industry, which generates $150 billion each year, according to the International Labour Organization.
Digital divide
The digital revolution has connected billions of people to the internet and paved the way for transformative technologies that have improved countless lives. Yet many individuals living in developing countries have been left behind as high-speed broadband access remains practically out of reach to entire nations and regions. This digital divide is holding back the economic growth of poorer countries as they are unable to take advantage of the opportunities found in the global digital ecosystem.
Communications firms are helping reduce digital inequality by providing access to broadband, through their satellite-enabled Community Wi-Fi service, to isolated regions across the world.Internet service that can be deployed with minimal local infrastructure investment and low ongoing costs, making it simple for these communities to fund through the increased economic opportunities the connection offers.
Big data technologies are fundamentally changing how companies in all industries operate due to their ability to provide actionable business insights and better understand customer behavior. By applying these same data interrogation techniques to publicly available records, both governments and private enterprises can gain a clearer understanding of how to deal with pressing social issues.
Animal Testing
Animal testing is still widely used in drug trials with governments, animal welfare charities and other non-governmental organisations maintaining pressure on major pharmaceutical companies to reduce the number of tests carried out. To understand the extent to which animal tests are predictive of human response, pharmaceutical firm Bayer and information company Elsevier analysed more than 1.6 million public records.
The findings have considerable implications for improving patient safety, can help pharmaceutical firms decide which tests are appropriate and which might be ruled out to reduce unnecessary testing on animals. This will hopefully encourage other researchers to examine public data to see what other socio-economic issues can be addressed through proper data analysis.
Disease Control
Big data is often discussed in the context of improving medical care, but it also has a less appreciated but equally important role to play in preventing disease. Big data can facilitate action on the modifiable risk factors that contribute to a large fraction of the chronic disease burden, such as physical activity, diet, tobacco use, and exposure to pollution. It can do so by facilitating the discovery of risk factors for disease at population, sub-population, and individual levels, and by improving the effectiveness of interventions to help people achieve healthier behaviors in healthier environments. In this article, we describe new sources of big data in population health, explore their applications, and present two case studies illustrating how big data can be leveraged for prevention. We also discuss the many implementation obstacles that must be overcome before this vision can become a reality.
The technological underpinning of health-focused big data is the use of sensors and smartphones to track various aspects of health and health behaviors. People are increasingly interested in tracking their health through mobile health sensors and applications, and have the requisite technology experience to do so. According to a Pew report on the social life of health information, 27% of internet users age 18 and older track their own health data online, with 15% having tracked their weight, diet, or exercise routine and 17% having tracked any other health indicators online. Another Pew report found that 29% of American adults who download apps to their smartphones have downloaded an app that helps them track or manage their health.
The number of mobile health application users is growing rapidly and is expected to reach 247 million users by the end of 2012. Disparities in smartphone and technology access are important to address, however, when considering their utility in health data collection and intervention. The most recent Pew report found that 91% of all adults in the United States own a cell phone, with 56% owning a smartphone. When segmented by race, 53% of whites, 60% of Hispanics, and 64% of African American adults own smartphones. While only 43% of those earning less than $30,000 a year own a smartphone, ownership increases to 77% when considering those under age 30 in this income group.
Responding to this interest, the market for personal sensors, health applications and their combination has rapidly expanded. As smartphones have become widespread — and, for some, indispensable — in modern life, they can serve as important passive and manual data collection devices. Projections estimate that 50 billion devices will connect to the internet in the next 10 years, generating 40-fold the current amount of global personal data. Passive data collection through the phone’s own accelerometer and other sensors make data collection automatic and effortless. The velocity, variety, and volume of these new big data sources make them particularly relevant to both health research and interventions.
Artificial intelligence tops the technologies used by China to handle the coronavirus outbreak. Technology companies in China are developing applications to help people confirm their movements during the period of the outbreak as a safety measure and to avoid further spread. Data from trains is an example of passenger screening where applications check the movement and contact of people.
Secondly, tech companies in China are analyzing passenger flight information with other cases such as in Guangzhou where AI robots are reminding people to wear masks for disease prevention. These initiatives demonstrate the technological solutions applied by China in the midst of this crisis. Predictive analytics from data is changing the approach of outbreak management by delivering updates which are then followed by additional updates. Data analytics, AI and machine learning have been pivotal in addressing the coronavirus outbreak and according to WHO.
The Value Of Imaging Data As The Missing Essential Component Of Real World Data
Data Intelligence networks such as DDAM is what will make humans , the dominant species on the planet evolve how we think. DDAM is the most powerful instrument around. We are embodying that kind of intelligence in increasingly sophisticated Digital Data Asset Management and are coming to depend on them more and more over time and we’re headed in the direction of building technologies that are at the human level and, eventually, far beyond that.
We’re not talking about the narrow forms of AI like the one that drives the Google car , we are talking about Data that helps the doctor make diagnoses or helps manufacturers increase their activity or individual track their day to day progress in form as a digital data asset. Those are all very specialized forms of data that will bring value to the source owner.
The human race is becoming so intelligent that we can perform an infinite variety of tasks across domains of activity. We’ll continue to become smarter and more capable and more powerful to help ourselves and one another to thrive. DDAM , A next generation of Data Economy that can transform Data Resource into Data Asset which are data resources that can generate value for the data owner. Through scientific and effective data asset management tools to realise the full value of their own data assets.
This article is written by one of of our valued community members : Kerwin
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