In an era where climate change is one of the main challenges for our future, it is crucial for companies to apply favorable environmental practices. Customers look for eco-friendly businesses and employees want to work for ethical companies.
At the same time as companies embark on their sustainable journeys, data analytics is emerging as a transformative force in business. Both concepts may seem to have nothing in common, but there has been an increasing number of organizations applying big data to improve their sustainability. Data analytics allows companies to measure their environmental impact, optimize resources and drive impactful environmental changes.
These past months I have been working for a pioneer company in adopting sustainable IT practices to measure and reduce its impact on the environment. Since 2021, it has been voted every year the world’s most sustainable coffee company.
Thus, I would like to take this opportunity to talk about how Big Data and analytic tools are used to improve sustainability. This article explores diverse ways in which data analytics empowers companies to become more sustainable.
Energy efficiency and resource management
One of the primary applications of data analytics in sustainability is improving energy efficiency. These tools allow organizations to collect and process multiple data related to resource consumption and carbon footprint to identify which elements of the company are responsible for the most emissions. This way they can implement actions to reduce energy consumption, carbon footprint and develop predictive environmental models.
For example, Google uses data analytics to manage its data centers’ energy consumption. They use machine learning algorithms to analyze vast amounts of operational data from its data centers. These models process data on temperatures, power usage, cooling efficiency and workload distribution, which allow them to apply the most energy-efficient configurations. With this approach Google has reduced energy usage by predicting the optimal cooling configurations, resulting in an important reduction in energy needed for cooling.
Supply chain optimization
Supply chain is a significant area where data analytics can drive sustainability. Through data analysis, companies can track the efficiency of the different steps in their supply chain. They can optimize inventory management by providing real-time insights into demand patterns, inventory levels and lead times. This data also allows them to optimize transportation routes and identify the best paths of transportation, resulting in critical reductions in emissions, fuel consumption and transportation costs.
Walmart employs data analytics to optimize its supply chain reducing greenhouse gas emissions. They use advanced algorithms and GPS devices to gather data related to delivery schedules, traffic patterns and fuel consumption. In addition, their real-time inventory management system allows them to track inventory levels, sales patterns and demand forecast. By analyzing all this data Walmart has been able to improve fuel efficiency, reduce overproduction and excess inventory, and decrease its carbon footprint significantly.
Sustainable product design
The design of environmentally friendly products is one of the areas in which most industries are focusing their efforts. Data analytics plays a key role in evaluating the environmental impact on products throughout their entire life cycle. By evaluating data on materials, energy consumption and waste generation, organizations can take actions related to product design and development. The use of these tools helps businesses to create more sustainable products, use biodegradable materials and reduce excess packaging.
Nike uses data analytics to design sustainable products, such as Flyknit line of shoes. First, advanced analytics helped them to gather and analyze biomedical data from athletes, including foot movements, pressure points and performance metrics. Then they used data analytics tools to analyze material usage and waste patterns in traditional shoe manufacturing, allowing them to detect inefficiencies in material cutting and assembly processes. With all these insights they were able to design and create a lighter shoe with less environmental impact. The result is that Flyknit manufacturing process reduces material waste by about 60% compared to traditional methods.
Sustainability, goal setting and tracking
Setting and tracking sustainability goals is essential for continuous improvement. By gathering, collecting and measuring data from multiple sources, data analytics tools enable real time monitoring of the company’s environmental impact, allowing organizations to make fast and data driven decisions. Thus, this approach ensures accountability and facilitates continuous improvement in sustainability practices.
For example, Johnson & Johnson uses data analytics to track its progress towards sustainability goals. They use sensors and Internet of Things (IoT) devices to gather data on energy consumption and carbon emissions, feeding this data into analytical models that suggest optimization actions. With this approach they can adjust their strategies, adopt new technologies and refine their goals to ensure ongoing progress.
Environmental trends forecast
The environmental impact of the company should not only affect current processes, but should also take into account future predictions. Data analytics can assist with accurate forecasts by analyzing market trends and historical data. Thus, the optimization of ecological processes involves the application of data-based predictive models that can identify potential environmental problems before they occur, allowing organizations to take actions to ensure that certain risks do not occur in the future or at least minimize their impact.
IBM’s Environmental Intelligence Suite uses data analytics to forecast environmental trends, such as extreme weather events, and assess risks to operations and supply chains. On- demand historical weather data provides access to past weather data to inform predictive models. This allows the company to prepare and mitigate the impact of environmental changes, ensuring business continuity and reducing potential environmental harm.
Final conclusions
Data analytics has emerged as a crucial tool for companies to become more sustainable by providing actionable insights into energy usage, waste management, supply chain efficiency and more. By harnessing the power of data, organizations can gain valuable insights into their operations and market trends. And through predictive modeling and data-driven strategies, businesses can optimize resource allocation, reduce waste and contribute to environmental management.
It is true that integrating data analytics presents several challenges that can vary depending on the size and resources of a company. The complexity and the financial investment required to implement these tools are significant and these costs can be a major barrier for
most businesses. In addition, they require an advanced level of expertise to interpret data and translate it into actionable insights. Thus, the integration of data analytics into sustainability should be done as a strategic investment that aligns with business objectives, ensuring that the benefits justify the costs and complexities involved. This not only benefits the environment but also drives cost savings and improves their competitive advantage.
-J.C