Leveraging Data Analytics: Sustainability is just like any large transformation challenge – it’s a data and insights problem and requires careful change management.
The Perspective
Sustainability is a paradigm for thinking about the future in which environmental, societal and economic considerations are equitable in the pursuit of an improved lifestyle. Most of the economies are developing with breakneck velocities and are becoming epicenters of unsustainable global growth. Immense utilization of natural resources, waste generation and ecological irresponsibility are the reasons for such a dire situation. Big data analytics is clearly on a penetrative path across all arenas that rely on technology.
Today, business intelligence remains vital. The term refers to the transformation of data into knowledge that can support business decisions. As Environmental, Social, and Governance (ESG) issues become central to business strategy, companies require new types of non-financial business intelligence. Big data analytics offer intriguing solutions that can guide companies’ sustainability strategies
Big Data and Data Analytics
Everything around us is impacted by big data today. The phenomenon took shape earlier in this decade and there are now a growing number of compelling ways in which big data analytics is being applied to solve real-world problems. This big data revolution, which encompasses techniques to capture, process, analyse and visualize large datasets in a rapid timeframe, has led to an explosion in data variety over the last five decades.
With the increasing importance of environmental sustainability, organizations are looking to drive systematic improvements across their value chain by leveraging a solution framework that enables analytical interventions to deliver end-to-end sustainability capabilities as a service. Organizations need data and analytics as a key enabler to move from managing environmental impacts to the entire value chain and total impact perspective.
Sustainability and Sustainable Development
One of the most frequently used terms is sustainable development. It was first introduced in the Brundtland Report in 1987 and described as – Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. Three equally important areas also characterize sustainability. These comprise environmental, social, and economic factors. The goals, needs, and problems in all of these areas have to be addressed. Only then can the sustainable development of the planet, society, and the economy be ensured to tackle social issues.
Data Science and sustainability
Data Science and Sustainability are two buzzwords that seem to be everywhere. Everyone wants to do something with data.Tech companies aren’t the only ones who have been leveraging data to optimize processes, generate valuable insights, and create new products. At the same time in the sustainability arena, governments, companies, and individuals are trying to tackle the climate crisis. People are working towards a more just future with less inequality and more prosperity for a larger share of the world population.
However, a growing number of organizations and individuals are demanding that Data and data science should be applied to solve the existential problems we are facing. More and more platforms promote data for societal benefits. As companies seek new ways to integrate sustainability into their strategy and business models, data-driven solutions are evolving to meet those needs. new data technologies will come to assume a central role in sustainability strategy.
We know that what gets measured, gets managed, So, this kind of technologies will allow companies to measure and manage their impact not only on their shareholders, but on the diversity of stakeholders with which they interact. Sustainability isn’t going away, Instead, it’s becoming more complex, and data analytics can give companies the business intelligence they need.
These issues are becoming more and more core to strategy. They are not peripheral to strategy. They’re not an add-on. I do not think this is going to be a nice technology to have, but it’s going to be a necessary technology, especially for those companies that are really genuine and serious about their commitment towards stakeholders and towards having a positive impact on society. In other words, sustainable companies.
Advantages of Data Analytics
Big data can generate useful insights that can help foster environmental sustainability. One of the key advantages of data analytics lies in its ability to help industries understand and act on the environmental impacts of their operations. This process can lead to knowledge that can improve decision making, refine goals and focus efforts. Previously, this information was dispersed across multiple platforms and in different formats. Businesses are now trying to make out the end-to-end impact of their entire operation.
Data analytics has also proven useful in optimizing resource usage thus providing a more efficient and sustainable means of operations. Even minor improvements in efficiency due to resource optimization can result in big savings and a lessened environmental impact. Data analytics can be used to anticipate supply and demand, which in turn can be used by business to resource their needs in advance at a more competitive price. Business can also utilize big data to analyze past performance against current progress and provide valuable insights for future sustainability initiatives.
Data analytic approaches to business intelligence also have pitfalls. Data may be unreliable, or can distract companies from answering questions that are more important but harder to answer. Companies are developing different ways to grapple with such challenges
How can data science support sustainable development ?
Emerging data analytic technologies can address the challenge of ESG issues that are material to companies, flag risks, benchmark a company’s sustainability reporting against competitors, monitor media references to strategic issues, and analyze sustainability best practices. Data science can be leveraged in various ways to enable sustainable development. The below examples are linked to measuring impact, managing resources, climate change, and health & equality
- UN has developed 17 Sustainable Development Goals. It’s good to have goals. But you need to quantify relevant metrics to determine your progress. That is why SDG Tracker has been developed. It leverages data from the UN (also available via an API) and other international organizations. The tracker provides data visualizations and explanations of the indicators with the express purpose of holding governments accountable to their commitments.
- However, several goals cannot comprehensively be assessed because data is missing. This highlights the data availability problems that exist for these macro indicators. The development of Open Data platforms and applications is therefore crucial. Data collection and turning available data into machine-readable formats are necessary to map the status quo. The Global Partnership for Sustainable Development Goals is one institution that supports and coordinates these efforts.
- Satellite imagery is used to gather data related to poverty and hunger reduction. This data is otherwise difficult to collect. It is possible to estimate crop yields based on weather conditions and crop growth. Particularly vulnerable populations can be identified, and help can effectively be targeted.
- Predicting plastic waste available for recycling can fill a significant data gap. Companies need to understand available quantities better. Only then can more recycled plastics be used for new products. The current market for recycled plastics suffers from a lack of transparency and information.
- Personal transportation with cars needs to be reduced and replaced by public transport, cycling, or walking. Mobility data can be used to analyze travel patterns. These can inform public transportation infrastructure and make them more user-friendly. In urban areas, schedules, capacities, and available transportation options could be adjusted. This might motivate more people to switch to more sustainable transportation modes.
- Data science can help create a more sustainable energy sector in multiple ways. One example is the support of smart grids through dynamic energy management. This involves production planning and forecasting, for example.
- Machine learning helps investors choose the most socially and environmentally sustainable companies which are profitable at the same time. Moving funds away from polluters and towards socially responsible companies helps finance sustainable business practices and pressure laggards to do more.
- The 50×2030 Initiative helps lower-income countries build agricultural data systems. These contain information from household and commercial farms. The data can be used to inform policy decisions and target agriculture investments optimally. The ultimate goal is to combat hunger.
- The SABER project by the World Bank gathers and evaluates data on education systems. It is aimed at increasing the quality of education. Countries can strengthen their education systems through policies informed by evidencebased standards.
- Big data can also be integrated into government policies to ensure better environmental regulation. Governments can now implement the latest sensor technology and adopt realtime reporting of environmental quality data. This data can be used monitor the emissions of large utility facilities and if required put some regulatory framework in place to regularize the emissions
- Analyses of big data are clearly essential for highlighting declines in Earth’s environment and its capacity to support humans. Yet, these impressive advances will not benefit the planet and its people unless we can act to achieve sustainability goals, so it is essential that big data coalesce with ongoing efforts to achieve sustainability
- Automating data collection and analysis so companies can make better decisions and meet their climate-action commitments. Using predictive analytics to maximize efficiency of transportation and boost sustainability Enhancing supply chains to minimize disruptions and cut carbon emissions and waste.
Conclusion
Data is the lifeblood of decision-making and the raw material for accountability. Today, in the private sector, analysis of big data is commonplace, with consumer profiling, personalized services, and predictive analysis being used for marketing, advertising and management. Similar techniques could be adopted to gain real-time insights into people’s wellbeing and to target aid interventions to vulnerable groups.
New sources of data – such as satellite data -, new technologies, and new analytical approaches, if applied responsibly, can enable more agile, efficient and evidence-based decision-making and can better measure progress on the Sustainable Development Goals (SDGs) in a way that is both inclusive and fair.
A lot of work still lies ahead of us to ensure that future generations will live peaceful and prosperous lives on a healthy planet. The above examples show compelling ways in which data science can help. Maybe none of these options are relevant to you at the moment. A good first step was becoming aware of some of the environmental and social issues we are facing, and that data science can be applied to help solve them