Council Post: Data science and analytics for sustainable development
According to the 1987 Brundtland Report, sustainable development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The three important areas of sustainability include environmental, social, and economic factors. The United Nations has developed 17 sustainable development goals (SDG) around these areas. The success of these goals will ensure prosperity and peace for the planet, society, and the economy for today and in the future. Let’s take a look at each of these goals below: Data science and analytics to the rescue Data science and analytics have a key role to play in achieving these sustainable development goals. They can be leveraged to enable sustainable development, particularly measuring impact, managing resources, tackling climate change, and more. Let’s look at each of these verticals – i.e. economic, social and environmental, in line with 17 goals of sustainable development and how data science and analytics can play a role in achieving sustainability with a few examples. Solving economic crisis Investors can use data science to help choose more sustainable companies.. They can move funds away from polluters and towards socially and environmentally responsible companies that adhere to best practices. Data science can help lower-income countries and emerging economies build data systems that contain information about households, occupations, wages, etc. This data can be further used to inform policy decisions, target investments and budgets optimally, and increase a country’s GDP. Building sustainable cities and communities also becomes equally important. Data science can help in planning sustainable cities with better infrastructure and amenities. Creating a positive social impact Satellite imagery can be used to collect data related to poverty and hunger-reduction, where the data includes images of densely populated human colonies, absence of water resources, barren land, etc. Otherwise, this data is often difficult to collect. Through data science and analytics, it is now possible to estimate crop yields based on weather conditions/patterns and crop growth. With this, a vulnerable population group can be easily identified, and the government or authorities can take the necessary steps/actions to mitigate risks effectively. In terms of achieving quality education goals, data science can improve education systems in the country, thereby helping create informed policies via evidence-based standards. Data science and analytics can address the data gap on violent encounters between citizens and the police or religious groups. Data science and analytics help law enforcement and concerned authorities capture and report this data, which includes information of people who have previously instigated such conflicts, people who have misbehaved with the authorities previously, names of convicted criminals, etc. These data can help authorities in maintaining law and order of the place, and stop potential threats from occurring. Most importantly, this can help build trust between communities and authorities. Addressing environmental concerns Data science can help create more sustainable energy. For example, in smart grids through dynamic energy management, data science can be used for production planning and forecasting. Under clean water and sanitation, image recognition can sort waste in recycling facilities or plants. In the case of responsible consumption and production, data science can be used to predict plastic waste available for recycling. It helps companies to understand available qualities better. Also, it helps them use recycled plastics for making new products effectively. However, the present-day market for recycled plastics suffers from a lack of transparency and information. So, satellite data and image recognition can identify riverside and coastal plastic hotspots and help in taking necessary actions or measures. Concluding remark The above examples show compelling ways to achieve some of the sustainable goals using data science and analytics. But, there is always scope for more. India has slipped three spots from last year’s 117 to 120th position on the sustainable development goals adopted as part of the 2030 plan by 192 UN member states in 2015. So it’s high time the government and industry bodies started focusing on ways to build data-driven measures and initiatives to tackle sustainability. It is really important for all the stakeholders to become aware of some of these economic, social, and environmental problems that India is facing and leverage data science and analytics to solve them effectively and effortlessly. In our upcoming articles, we will dive deeper into understanding these goals and suggest data science and machine learning techniques to solve them. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
Data science and analytics have a key role to play in achieving these sustainable development goals. They can be leveraged to enable sustainable development, particularly measuring impact, managing resources, tackling climate change, and more.
Data science can help lower-income countries and emerging economies build data systems that contain information about households, occupations, wages, etc. It can address the data gap on violent encounters between citizens and the police or religious groups. Most importantly, this can help build trust between communities and authorities.
["environment", "sustainability"]
["data science", "Go", "machine learning", "AI", "sustainability", "image recognition", "RAG", "Aim", "analytics", "environment", "Rust", "R"]
["AI", "machine learning", "data science", "analytics", "Aim", "RAG", "image recognition", "R", "Go", "Rust"]
With close to 15 years of professional experience, Anirban specialises in Data Sciences, Business Analytics, and Data Engineering, spanning various verticals of online and offline Retail and building analytics teams from the ground up. Following his Masters from JNU in Economics, Anirban started his career at Target and spent more than eight years developing in-house products like Customer Personalisation, Recommendation Systems, and Search Engine Classifiers. Post Target, Anirban became one of the founding members at Data Labs (Landmark Group) and spent more than 4.5 years building the onshore and offshore team of ~100 members working on Assortment, Inventory, Pricing, Marketing, eCommerce and Customer analytics solutions.