Big Data for Sustainable Development

 

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If you're a sustainable development professional, you know how important big data is for determining how well a country is achieving its goals.

 It isn't just about collecting a lot of data and reporting it - it's about leveraging it to understand how your country is progressing. 

In this article, we'll discuss how to use big data to measure and monitor the Sustainable Development Goals (SDG). We'll also take a look at some case studies of countries using big data to track their progress.

 

Case studies of countries using big data for SDG monitoring

 

The role of big data in monitoring the Sustainable Development Goals (SDGs) is attracting considerable interest. This interest has led to several case studies on how countries are leveraging this technology. However, the underlying data sources are often inadequate. These shortcomings present challenges in the implementation of the SDGs.

 

The most common data source for monitoring the SDGs is official statistics. Unfortunately, some producers of official statistics do not have a clear understanding of how to measure the uptake of big data.

 

One solution is to leverage partnerships with knowledge brokers. These relationships can increase awareness of the value of big data. At the same time, these partnerships can transfer new skills to National Statistical Offices. Developing partnerships with convening organizations also builds trust in new processes.

 

Another big data approach involves the use of complex datasets. For example, Big Earth Data is a frontier technology that uses advanced techniques to monitor SDG indicators. It can be used to monitor marine ecosystem health.

 

A new concept for observing complex systems, such as the SDGs, is the Systems Innovation Approach. Through co-design, this approach enables better understanding of the relationships between concepts. In this context, the SustainGraph is a graphical tool that can highlight the weight of the relationships between concepts.

 

The latest research collaborations are generating innovative datasets by using big data. Among these is the Big Earth Data Science Engineering Program, which uses big data science techniques to monitor six SDGs.

 

As a result of these recent case studies, there is a growing interest in using data sources from partners who may not traditionally collect data. This has been accompanied by an increasing demand for accurate spatial information. Consequently, this has led to a growing need for new tools and data products to be developed.

 

These new tools will be able to extend their use to nations with little or no data collection capacity. This will help build a more robust foundation for the SDGs. Ultimately, this will provide a better framework for evaluating the SDGs' progress.

 

Non-traditional data sources are used to measure the progress of the SDGs

 

A key contribution to the achievement of the SDGs is the use of non-traditional data sources. This article identifies some of these sources and their role in advancing the SDGs.

 

Non-traditional data sources include web and mobile phone data, smart meter data, social media data, and geodata. These forms of data can help countries monitor SDG targets. They also provide a more timely perspective on progress towards the goals.

 

In many cases, these forms of data are lower cost than traditional data sources. Moreover, they are largely disaggregated, meaning that they break down the data into smaller subgroups. Using these forms of data can allow countries to develop evidence-based policy and make better decisions.

 

Non-traditional data sources are becoming more important in advancing the SDGs. While traditional data sources are still the primary source of information on the SDGs, they are being challenged by the proliferation of new and innovative data sources. The global initiative Data4Now aims to improve the quality and timeliness of SDG data.

 

Moreover, the SDG Innovation Lab is working to advance innovative data collection methodologies that can be scaled at the national level. It has also partnered with the UN Statistics Division to build capacity for the collection of SDG indicators.

 

As the world continues to face unprecedented challenges, it is essential to adopt smart strategies that capitalize on emerging data sources. This includes tapping the expertise of government agencies and other data experts.

 

National Statistical Offices (NSOs) struggle to meet the demands of a rapidly growing data population. This has created a complex environment of competing data sources. NSOs need to strengthen their capacities if they are to successfully achieve the SDGs. However, they often lack the funds necessary to produce the data needed.

 

Non-traditional data sources have emerged as a means to measure crucial demographic indicators. A number of NSOs have begun pioneering their application. For example, Senegal's National Agency of Statistics and Demography has partnered with the UN Food and Agriculture Organization and the UN Habitat.

 

Another case study is the DANE project. The project uses satellite images, administrative records, and other sources to calculate statistics.

 

 

Partnerships with data providers and technology experts remain important

 

Data and information technologies are playing a big part in reshaping the world we live in. And while big data has been around for years, recent advances in technology, particularly artificial intelligence and machine learning, are making this data a valuable resource. A better understanding of the data flows and associated privacy concerns could help shape more meaningful data-driven policies and technologies.

 

Big data has become a hot topic amongst policy makers, companies and data-hungry consumers. In many cases, it is the big companies with the big bucks who dominate the data flow space. But it is also the smaller, but no less powerful, companies that have the greatest potential for using their own or third-party data to drive innovative business models and solve the world's most pressing problems. As such, it is essential for governments to implement robust data protection and data management regulations in order to protect consumers and ensure that the big boys play by the rules. To that end, the European Union has been at the forefront of the industry since the advent of GDPR. This is a good thing, given the prevalence of data-driven digital tools in the workplace.

 

Despite the recent flurry of legislation, the data management industry still faces a number of challenges. The biggest challenge is ensuring the proper integration of big data into enterprise-level architectures. Developing such an nascent ecosystem will require substantial investments in technical expertise and infrastructure. Some of these efforts are being financed by the World Bank and the Global Partnership on Sustainable Development Data.

 

Nevertheless, the best way to achieve these goals is to rely on partnerships with providers of data and technology experts. These partners can assist in the identification and implementation of the most promising data-driven innovations. Hence, the need for an integrated strategy is a clear requirement for any aspiring data czar. While this may sound like a daunting task, it is one that can be mastered. Ultimately, this will ensure that data-driven innovations of all types and sizes are made a part of our daily lives in ways that are safe, effective and rewarding.

 

 

Privacy and security of big data

 

Data security is one of the key concerns in the big data environment. The volume of information in the world is growing exponentially. Businesses and government agencies are constantly collecting and storing huge amounts of data. Various technologies are under development to meet these demands. However, it is important to address privacy and security issues throughout the life cycle of the data.

 

Big data analytics is the process of discovering hidden patterns in data. This is a valuable tool for addressing a range of socioeconomic problems. But, if privacy is not protected, personal data could be accessed by unauthorized parties.

 

A major open challenge in the big data environment is the misuse of data analyses. For example, combining multiple datasets may lead to the re-identification of individuals. Moreover, there are various laws that are being adopted to protect big data privacy.

 

Several standardization organizations are currently working on the establishment of standards for big data. These include international and national standardization organizations. Nevertheless, it takes several years to establish these standards. In addition, there is a lack of detailed descriptions of technologies and requirements in published standards.

 

WG 9 Big data is a group that was formed in 2015 to develop standards for big data. Similarly, GB/T 37973-2019, a draft guide on the security and privacy aspects of big data services, is currently being developed. It specifies the basic security capabilities for big data services.

 

However, there are still gaps in the security and privacy of big data. Specifically, the collection phase does not have sufficient security practices to ensure the safety of the data. Some data fields should be encrypted in the collection stage. There should also be some limited access control.

 

Another challenge is the development of data destruction technologies. Currently, the main techniques are overwriting and degaussing. However, these methods cannot be applied to distributed environments.

 

In order to mitigate these challenges, developers should be able to verify whether their applications will meet the privacy agreements. In addition, a proper framework must be in place to clearly define roles and responsibilities.