To completely implement and analyze progress on Sustainable Development Goals, decision-makers require a lot of information and statistics which are perfect, timely, adequately disaggregated, applicable, accessible as well as pretty easy to use. Data accessibility and quality have gradually enhanced over the period. Though statistical volume still requires solidification, and when it comes to data literacy, it has to be evolved at every level of the decision-making process. It will need a lot of organized efforts on the part of data creators and users from numerous data solutions. Overcoming multi-layered barriers to sustainable development will also require innovative technologies to generate and implement data and statistics.
The UN Global Goals. Source: United Nations
With a host of SDG indicators to review, Big Data can easily supplement customary data sources to keep a better track of the development plans. The Sustainable Development Goals provide particular, time-bound, and computable objectives in collaboration with the national development strategies same as priorities. Though, with more than 230 SDG indicator; plenty of which need proper disaggregation by various parameters like location, gender, income, age, and a host of other appropriate dimensions - gathering the required granular data to review all SDGs as well as their objectives is not at all an easy feat for the existing national statistical systems (NSS).
In order to understand the level of capability of the national statistical systems specifically for the SDG era, The United Nations Economic and Social Commission takes into account the 22 nations’ experience in disseminating the SDG indicators as well as using a host of different varieties of data sources.
The national statistical organizations recently reported that disaggregation of the stats by location for plenty of different SDG indicators. Although, the disaggregation is pretty scant for a few of the SDG indicators. However, it is all the thinner when it comes to specifically the disabled population as well as the indigenous peoples.
Now, most of the NSOs have happily acknowledged that the only technique through which they will be able to fulfill SDGs’ disaggregated data needs is to make good use of innovative procedures and data sources.
Plenty of governmental bodies are already using small area estimation (SAE) techniques which are aimed to enhance the direct survey approximations particularly small areas (or tiny sub-populations) with supplementary data inputs like the census records). SAE methods help to attain more granular information on poverty or nutrition.
Some popular NSOs also provided information about their current level of existing photo/satellite images, phone data, web-scraped costs, online cost data, same as social media data. Plenty of the respondents see Big Data as a fruitful new way to address the not so happening data gaps for SDGs. However, just a very few numbers have Big Data projects at the moment.
Big Data gathered from a host of search engines, electronic gadgets, social media, as well as from a variety of sensors tracking gadgets and satellite images now offer a novel information source to the NSSs, which includes 3 Vs volume, velocity, as well as variety. And, this beautifully supports the statistics, which are gathered from traditional sources. Big data is currently being explored sequentially for many different types of development purposes.
A large number of the respondents see For example, in Jakarta, Twitter interactions on the cost of rice have offered a ground-breaking way to review the actual costs. In the Philippines, World Bank is working in collaboration with the ride-hailing service supplier to launch the Open Traffic Initiative. They are presently using the company’s driver data to get almost real-time traffic information and related statistics, like flow, speed, as well as information about the delays at intersections. This information will help to study critical parts of traffic management.
The United Nations Statistics Division has already developed a thorough record of Big Data projects. And this inventory contains both previous as well as the existing undertakings based on the making use of scanner information from the supermarket chains as well as other retailers. It also includes information about the online prices attained from web-scraping. It is used to generate price catalogs in many countries.
Big Data is a very fresh and innovative way to fill in the present data gaps for SDGs. It just that we have to expand its usage. More and more projects will also have to start using the power of Big Data analytics solutions as well.