Published: Oct 31, 2025

Efficient data management techniques for Green AI

The world is generating data at an unprecedented rate. An estimated 463 exabytes every day by 2025, according to the World Economic Forum^[1]. Managing, processing, and storing this deluge of information has become one of the greatest sustainability challenges of the digital era. As artificial intelligence grows more complex, the energy required to train, refine, and maintain models increases exponentially, intensifying the environmental impact of data operations.

Green AI provides a pathway to balance innovation with environmental responsibility, and data lies at its core. By rethinking how data is collected, stored, processed, and disposed of, organisations can cut energy costs, reduce emissions, and improve overall AI performance. Efficient data management is not just a technical exercise but a key enabler of sustainable AI systems.

NCS supports enterprises in building responsible AI foundations through data modernisation, cloud optimisation, and intelligent lifecycle management, helping organisations align their digital transformation goals with the sustainability objectives outlined in frameworks like the Singapore Green Plan 2030.
 

• By 2025, the world will generate around 463 exabytes of data daily ^[1], driving massive energy and storage demands.
• Conventional data management methods are inefficient, leading to waste and higher carbon footprints.
• Efficient data management — including compression, deduplication, and storage optimisation — can significantly reduce energy consumption.
• Feature selection techniques can lower AI training energy use by up to 76%^[4] while maintaining performance.
• Sustainable data management is central to Green AI, ensuring responsible, efficient, and ethical use of data.
• NCS helps enterprises modernise data ecosystems to achieve operational efficiency and measurable sustainability gains.
   

Data is the fuel of AI — but its management comes with a growing environmental cost. By 2025, it is estimated that 463 exabytes of data will be generated every day ^[1]. The storage, transfer, and processing of this data require immense computational power and energy. Conventional data management methods often rely on resource-intensive data centres powered by non-renewable energy sources, resulting in high operational costs and large carbon footprints ^[2].

In many enterprises, inefficiencies compound the problem. Data is often siloed, duplicated, or stored beyond its useful life, increasing both energy consumption and emissions. Figure 1 illustrates three common challenges in traditional data management practices — isolated data and repetitive processes, uniform tools across diverse use cases, and inadequate oversight— all of which contribute to waste and inefficiency.

Figure 1: Challenges in Conventional Data Management Practices.

Without intervention, the environmental impact of data operations will continue to rise, eroding the benefits of AI-driven transformation and making sustainability goals harder to achieve.
 

Green AI reframes data management as a sustainability enabler rather than a backend function. Sustainable data management practices optimise how data is collected, stored, and processed, ensuring that resources are used efficiently while reducing energy demand ^[3].

By adopting efficient data management techniques, organisations can reduce both their operational costs and environmental footprint. These techniques include:


Figure 2: Techniques for efficient data management.

1. Data deduplication – A method that identifies and removes redundant data, ensuring only unique instances are stored. Deduplication can occur at the file level or block level, significantly reducing storage requirements and improving efficiency.
2. Data compression – Reduces the size of data for more efficient storage and transmission. Techniques include lossless compression (e.g., Huffman coding, LZW), lossy compression (e.g., JPEG, MPEG), and hybrid methods (e.g., Burrows-Wheeler transform, Run-Length Encoding).
3. Storage optimisation – Organises and manages data to minimise resource usage. Examples include tiered storage models and intelligent data placement based on access frequency or energy efficiency.
4. Innovations in data processing and management algorithms – Uses energy-aware, adaptive, and machine learning–based techniques to optimise processing tasks, reduce computational load, and improve energy efficiency.

Together, these techniques reshape how data flows through AI ecosystems — minimising duplication, improving utilisation, and reducing the energy overhead of storage and retrieval.
 

When efficient data management becomes an integral part of Green AI, the results extend far beyond reduced carbon emissions. Organisations benefit from streamlined operations, improved model accuracy, and greater compliance with sustainability regulations.


Figure 3: Best practices for sustainable data management in Green AI.

Key outcomes include:

• Lower energy consumption: Through compression, deduplication, and tiered storage.
• Smaller environmental footprint: Reduced reliance on non-renewable energy and minimised e-waste through lifecycle management.
• Enhanced performance: Cleaner, better-organised data results in faster processing and more efficient AI models.
• Improved governance: Lifecycle policies and ethical data use strengthen privacy, transparency, and accountability.

For enterprises, the path to sustainable AI starts with their data. NCS helps clients design and deploy data-centric sustainability strategies that combine cloud modernisation, AI-enabled analytics, and data lifecycle management to ensure every byte contributes to business outcomes without unnecessary environmental cost.
 

Sustainable data management is the foundation of Green AI. By reducing duplication, compressing intelligently, and applying lifecycle thinking to data storage, organisations can dramatically cut the energy intensity of AI operations. Beyond reducing emissions, these practices create faster, leaner, and more resilient systems that directly enhance performance and profitability.

NCS partners with enterprises to transform how data is managed and used — helping them build energy-aware data architectures that align with the Singapore Green Plan 2030 and other regional sustainability goals. Through data modernisation, governance frameworks, and cloud optimisation, NCS enables organisations to balance growth with environmental stewardship — turning data efficiency into a measurable competitive advantage.
 

Explore how data efficiency fuels Green AI in our visual Green AI infographic.

References

^[1] How much data is generated each day? https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/ via @wef 

^[2] Future of Green Business Through Sustainable Data Management, https://www.allerin.com/blog/future-of-green-business-through-sustainable-data-management 
 
^[3] R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green AI,” Communications of the ACM, vol.63, pp.54–63,112020. 
 
^[4] R. Verdecchia, et al., "Data-Centric Green AI An Exploratory Empirical Study," in 2022 International Conference on ICT for Sustainability (ICT4S), Plovdiv, Bulgaria, 2022 pp. 35-45.