Eine Datenanalystin in einem Rechenzentrum.

Green Data Science: How Data Analytics Can Be Made Greener

Digital transformation has revolutionized the business world in recent years, helping companies become more efficient and competitive. One of the key components of this transformation is data analytics, which enables companies to collect and analyze data from multiple sources to make informed decisions and gain competitive advantage. However, the growth of data analytics and cloud computing has also resulted in a significant environmental impact that is often overlooked. In light of this, it is becoming increasingly important to establish greener data analytics practices within organizations. By implementing Green Data Science, companies can not only help protect the environment, but also reduce costs and gain a competitive advantage.

Why data analytics is an environmental problem

Although data analytics is a valuable tool for companies to improve their performance and provide better services to their customers, it also poses a significant environmental problem. Data analytics requires enormous amounts of computing power, which is provided in data centers and cloud infrastructures. These systems require continuous cooling and power to ensure they are operational around the clock. This means that data centers consume a significant amount of energy and therefore generate a significant amount of greenhouse gas emissions.

It is estimated that the IT industry worldwide is responsible for about 2% of global greenhouse gas emissions, which is roughly the same proportion as the aviation industry. Data analytics alone is expected to account for about 5.5% of global electricity consumption by 2025, contributing significantly to climate change.

Another problem is that many companies do not have environmentally friendly methods for disposing of hardware and electronic equipment that can no longer be used. Disposing of old equipment often results in soil and water pollution from toxic chemicals and heavy metals. It is therefore essential that companies are aware of how to responsibly dispose of and recycle their old equipment.

However, there are also positive examples. Some leading technology companies have recognized that they have a responsibility to go greener and are working to reduce their energy consumption and emissions. Google, for example, has already announced its intention to be carbon neutral by 2030. Meanwhile, Apple has already converted its entire operations worldwide to 100% renewable energy.

A few numbers about data centers (example: Germany)

To illustrate the extent of the problem, here are some figures from the Bitcom study Data Centers in Germany – Current Market Development, Status 2022:

  • Between 2016 and 2021, data center capacity measured in IT connection lines grew by 30%.
  • Between 2016 and 2021, cloud capacities grew by 150%.
  • Currently, an estimated €2.5 billion is invested annually in data center infrastructures (buildings and technical building equipment) (increase of 150%).

All in all, a significant increase in the importance of cloud solutions is expected. Statista cites a positive message: Thanks to renewable technologies, the energy demand is increasing, but at the same time the CO2 emissions caused by the data centers are decreasing. These have been reduced in recent years without fear of energy losses. It remains important that companies take steps to make their data analytics practices more environmentally friendly.

Case studies and examples for Green Data Science

Some companies have already implemented Green Data Science in their business processes. Resulting in positive impacts on both the environment and their business processes. Here are some examples:

  • Google has the goal of switching completely to renewable energy sources by 2025. In this context, the company has also improved the energy efficiency of its data centers. It is using machine learning technologies to optimize energy consumption. This has helped Google use more renewable energy in 2017 than the company consumed in total.
  • IBM has developed a sustainability strategy that includes Green Data Science. The company is using virtualization technologies and machine learning models to reduce energy consumption in its data centers. As a result, IBM has already achieved a 5.5 percent reduction in energy consumption per year.
  • Microsoft has set itself the goal of being climate-neutral by 2030. The company is also relying on Green Data Science to reduce energy consumption in its data centers. By optimizing computing processes, Microsoft has already achieved energy savings of 29 percent.
  • Salesforce has a goal of switching to 100 percent renewable energy by 2022. The company is also relying on Green Data Science to optimize energy consumption in its data centers. Salesforce has already achieved energy savings of 62 percent by implementing virtualization technologies and more efficient cooling systems.

Use our tools for sustainable software

Our three tools help you acquire the knowledge, skills, and financial resources you need to develop sustainable software products and services that meet social, environmental, and economic needs. By using these tools, you can position yourself as a leader in sustainable software development and help create a better future for all.

Future prospects of Green Data Science

Green Data Science is an important trend in the IT industry and is expected to become even more important in the future. Some developments and changes can be expected in the following:

  • Advances in technology, particularly in the fields of artificial intelligence and machine learning, will make it possible to develop even more efficient methods for energy optimization. The application of algorithms and models that predict the energy requirements of IT systems and automatically implement appropriate measures will advance even further in the future.
  • The demand for sustainability in business is expected to continue to grow. More and more companies are turning to sustainable business practices to improve their environmental footprint. And to attract customers and investors. Green Data Science can play an important role in this context to reduce energy consumption in companies and to develop sustainable IT solutions.
  • Improving data quality will make it possible to make even more accurate predictions and further increase the efficiency of IT systems. Companies will be able to gather even more detailed information about their energy consumption and implement appropriate energy optimization measures.
  • Green Data Science will also open up new business opportunities in the future. Companies that rely on sustainable IT solutions can position themselves as pioneers in sustainability and thus gain a competitive advantage. New business models based on the processing and analysis of environmental data are also likely to gain in importance.

How Green Data Science Works

Green Data Science means doing data analysis in a more environmentally friendly way by using technologies and practices that reduce the energy consumption and environmental impact of data analysis. There are several technologies and practices that can be used in Green Data Science. Some of them are described below:

  1. Cloud computing platforms can help optimize computing resources and reduce energy consumption by allowing companies to use only the resources they need and scale them as needed. In addition, by using cloud computing platforms, enterprises can also reduce the hardware and energy costs associated with maintaining data centers and provisioning servers.
  2. Virtual machines allow businesses to run multiple applications on one physical machine. This allows organizations to reduce energy consumption and optimize resource utilization by maximizing the utilization of physical machines.
  3. An important aspect of Green Data Science is the use of energy-efficient hardware. Companies should invest in energy-efficient hardware such as energy-efficient servers and network devices to reduce energy consumption and operating costs.
  4. Use energy-efficient algorithms. Keyword: Green Coding.
  5. Companies should consider data-centric approaches where data is processed and stored in close proximity to users. This can help reduce energy consumption by avoiding unnecessary transfers of data.
  6. Another way to reduce the energy consumption and environmental impact of data analytics is, as the name implies, data reduction. Organizations should focus on capturing only relevant data to reduce storage requirements and lower the energy consumption of data analytics.
  7. Green Data Science should be integrated into a sustainability management system to ensure it is part of the company’s overall sustainability strategy. This can help ensure that Green Data Science is part of the company’s overall goal to become more environmentally friendly.

Green Data Science is an important step for companies looking to green their data analytics. By implementing technologies and practices that reduce energy consumption and minimize environmental impact. Companies can optimize their data analytics while making a positive contribution to protecting our environment.

Supplementary tips for the implementation of Green Data Science

Green Data Science can be challenging for organizations, especially if they don’t have experience with green data analytics. Here are some tips for implementing Green Data Science in your organization:

  1. Create a comprehensive strategy for Green Data Science. Identify areas where your data analytics can be greened and develop action plans to achieve those goals.
  2. Train your staff on the importance of Green Data Science. Ensure that they are familiar with the technologies and practices required for green data analytics.
  3. Regularly review your progress in implementing Green Data Science and adjust your strategy accordingly.
  4. Use nearby data centers that rely on renewable energy sources.


Green Data Science offers companies an opportunity to develop more sustainable solutions in data analytics. By implementing Green Data Science, companies can save energy while reducing their environmental impact. Companies that integrate Green Data Science into their business practices can not only improve their environmental footprint. But also create new business opportunities. It is important that we all take responsibility for the environment and make sustainability an integral part of our business processes.

Sources (for deepening)

  • Andrae, A. S. G. & Edler, T. (2015). On Global Electricity Usage of Communication Technology: Trends to 2030. Challenges, 6(1), 117–157. https://doi.org/10.3390/challe6010117
  • Andrae, A. S. G. (2019). Prediction Studies of Electricity Use of Global Computing in 2030.
  • Helmrich, K. (2021). Wie die Cloud, Edge Computing und Künstliche Intelligenz zur Nachhaltigkeit in der Industrie beitragen. CSR und Digitalisierung (S.175–192). Springer