Cohesity has analyzed the issues faced by the exponential growth of data worldwide, resulting from cheaper storage, cloud growth, and AI. Data center energy and efficiency are not keeping pace.

• In Europe, electricity is still generated mainly using fossil fuels; the energy required for the data economy produces large quantities of the most significant GHG, CO2
• Modern data centers consumed 0.3 kW/h per billed GByte in 2018. In 2020, 29.8 TW/h were used
• In OECD Europe, up to 217.98 million cubic meters of water were used in 2020. For 2030, researchers for OECD Europe expect:
  • With 112.7 TW/h of energy, 547 million cubic meters of water are needed
  • Water consumption in Europe will increase by 250 percent in ten years
  • Modern hardware is optimised for power, however, new use cases are pushing these platforms, AI for example, its hardware requirement is data and power-hungry. instead of 8.4 kw/h per rack, an AI rack needs up to 30 kw/h

The amount of data is already growing by an average of 50 percent per year in more than half of all companies, and the majority of organizations have infrastructure crammed with data, where 70 percent of the content is unknown on average. This uncontrolled growth has led to new terms being defined. The General Conference on Weights and Measures has expanded units of measurement for the first time since 1991 - Quetta and Ronna have now replaced Yotta as the largest unit prefix. A Quettabyte is a quintillion, 1000 Yottabytes (the largest scale so far) is 1000 Ronnabytes, and 1000 Ronnabytes is a Quettabyte.

“We are now living in the quettabyte era”, said Mark Molyneux, EMEA CTO at Cohesity. “However, there are actions that organizations can take today to reduce their data volume and undergo a ‘data diet,’ such as indexing and classifying data according to its content and value for the company. Everything that is without value can be deleted. Obsolete data, duplicates of systems, orphans, outdated test systems. You can also reduce data volumes using technology such as DeDuplication and Compression to eliminate redundant copies and automatically replace original data with a thin version, achieving reduction rates of up to 97% are possible. Classification according to your Relevant Records policy enables data owners to make the right decisions. This will allow Defensible Deletion Decisions, which you need to make but have been unable to do due to a lack of data intelligence. You will keep only what you need for the prescribed period and then automatically delete it. This will reduce your mountains of data, it will also give you vital intelligence when you experience a Cyber Event and need to know what has been compromised, or encrypted, or taken. AI and machine learning can truly enable the defuse of complex problems, and their LLMs are empowered by solid data.

This new infrastructure's exponentially growing energy hunger runs counter to the political goals and objectives of numerous global and European initiatives such as COP26 and the “European Green Deal” of 2020. The aim of the Green Deal is to make Europe climate-neutral by 2050. This initiative is being driven forward with the “European Digital Strategy” to ensure that data centers are climate-neutral by 2030. The International Energy Agency says emissions from data centers worldwide must be halved by 2030. This was before the sudden expansion of AI started to push computing and data volume.

While AI is data and power-hungry, alongside machine learning, it can help defuse one of the most complex problems, “unknown” data. Predefined filters immediately fish compliance-relevant data such as credit cards or other personal details out of the data pool and mark them. Once loose on the data, the AI develops a company-related language, a company dialect. And the longer it works and the more company data it examines, the more accurate its results become. Companies will be enabled to automatically identify obsolete, orphaned, and redundant data that could be deleted immediately.