Among the other concerns that the United Nations Framework Convention on Climate Change (UNFCCC) has addressed in its global treaty, the Paris Agreement, is a heavy emphasis on carbon footprints. The international treaty is sure to inspire stricter legislation and regulations against carbon emissions across the globe. For any digital-driven business that has not yet given any conscious thought to sustainability, this serves as a wake-up call for any business dealing with digital ecosystems.
When data is deemed new gold or new oil, the resources invested in polishing and sharing such highly-valued commodity add a lot to the industrial CO2 output. Therefore, it is natural that these concerns are bulleted in our data management agendas as well.
In this blog post, we will discuss all the different aspects of sustainable data management and try to figure out how it can effectively help businesses reduce their carbon footprint.
Unsustainable Data: What We Owe to CO2
The monthly carbon emission of ChatGPT is now attributed to 260 transatlantic flights. As a general rule of thumb, it is always a red flag if the carbon footprint for any technology rises to a level where it’s being compared to airplanes! However, such is the impact of a data-driven world where social media posts can affect geopolitical narratives, cryptocurrencies are becoming more dogmatic each day, and Marvel gets to churn out 5 movies a year. Data is being poured into sophisticated systems from every possible source and channel. Studies suggest that the power consumption for data centers will increase by 80% from 2022 to 2026.
Therefore, before we discuss sustainable data management, let us first understand the desperate need for it. Some environmental impacts of data management directly affect industrial CO2 usage:
- Electricity Consumption: Training and running large-scale data models like those for generative AI, business intelligence, cryptocurrency, and more is highly electricity-intensive. McKinsey suggests that the power consumption of data centers in the US was somewhere between 150 and 220 Terawatt-hours in 2024. This leads to heightened levels of carbon-dioxide, thanks to the generation and distribution of such large amount of power.
- Cooling Systems: From an environmental perspective, data centers can be considered temperature-controlled facilities that need high-end cooling systems to regulate the heat from their computing and networking resources. Apart from electricity, these cooling systems also need hydrofluorocarbon (HFC) based refrigerants that further add to the carbon footprint.
- Manufacturing and Transportation: Beyond the operational aspect, managing data also needs the production and transportation of a lot of computing hardware. Resources like GPUs, AI workloads, networking hardware, and more require mining raw materials, international transportation, and hours of fabrication process. Therefore, the more scalable your data management is required to be, the more carbon emissions will emerge due to these processes.
Sustainable Data Management: Roadmap for the Footprints
While national and international governance bodies do their bit, certain practices can be followed at a business level to handle and minimize the environmental harm caused by data management. Sustainable data management is a set of practices that help maintain data efficiency, cost-effectiveness, and utility without ignoring environmental responsibilities. From reducing redundancy in storage to enforcing compliance with environmental regulations, these sustainable practices can help us reduce the carbon footprint with our data strategies.
Responsibilities for Data Centers
The biggest onus lies on data center operations, as these employ the most resources that can affect the carbon footprint. They need strategies to improve Power Usage Effectiveness (PUE), adopt alternative power sources, ensure transparency through ESG reporting, and more. Some practices include:
- Mandating energy-efficient hardware like low-power processors, low-power SSDs, power-saving networking equipment and more
- Using renewable energy sources like solar and wind as much as possible, especially when processing non-urgent data
- Employing AI-driven monitoring to detect resource inefficiencies that may contribute to higher carbon emissions
Improving Data Storage
The resources employed for data storage also impact the energy consumption related to data management. With large volumes of data being stored and fetched from these storage units, specific practices should be employed to reduce their CO2 contributions:
- Deploying high-density storage solutions like SSDs and NVMe to reduce power consumption and heat generation
- Archiving infrequently accessed data into cold storage or low-power storage systems
- Employing methods like data tiering and waste-hear recovery to optimize energy usage
Data Resource Allocation with AI
AI and automation can play a very important role in directly affecting the carbon footprint for data management by offering customized and smart strategies. More than anything else, AI can help data admins to optimize resource allocation for data management. Here’s how:
- Balancing workloads by AI-driven automation of power distribution
- Leveraging predictive analytics for eliminating resource wastage based on historical trends
- Implementing AI for automating cooling system scheduling for various data resources
Managing Data Workloads
A lot can be done even with data workloads where day-to-day practices can make a huge difference in sustainable data management. Many data-related operations can be tweaked from scheduling to storage to sharing to ensure minimal carbon pollution.
- Scheduling and prioritizing tasks in ways to balance between peak hours and the consumption of non-renewable energy
- Running multiple applications on shared infrastructure like containers and VMs to maximize hardware efficiency.
- Minimizing redundant data storage to optimize storage and processing power
Conclusion
In one of the more standout scenes of the Oscar-nominated movie Conclave, Cardinal Lawrence suggests that faith walks hand-in-hand with doubt. The statement stands true to our faith in data as well. If we want our data to guide us through tomorrow’s problems, we must address the doubts raised against its management today. Sustainable data management will address these doubts and resolve them effectively. With the right practices, our data can lead us into a future not riddled with grimy carbon footprints.
