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Businesses not exploiting cloud’s machine learning power

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New research by Exasol has revealed that only 30% of organisations have invested in on-demand cloud processing to grow their artificial intelligence (AI) and machine learning capabilities.

The ‘Driving the Rise of AI and ML with Data’ report, conducted by research firm Vanson Bourne, found that 48% organisations now regard machine learning as very important in the near future, with artificial intelligence close behind. Applying predictive analytics, which relies upon machine learning to mine large datasets and predict the outcome of future events, was the main motivation with 64% of organisations regarding it as important.

Despite this immediate focus on improving machine learning and AI capabilities, the research suggests organisations are stockpiling data, but are unprepared for the data processing required. 37% of businesses have invested in cloud services purely for storing and consolidating their data, but only 30% of organisations have exploited the elastic scalability of cloud providers such as AWS and Azure to derive value from their organisation’s data.

Mathias Golombek, chief technology officer at Exasol commented, “I’m surprised that so few businesses have applied cloud infrastructure to machine learning and AI given its importance for enabling these techniques. It appears that many organisations are approaching data investment backwards and investing in the cloud for passive data services, when they could already be harnessing that scalable processing power to derive value from their data with machine learning.”

Golombek added, “It is essential that data processing follows the data across hybrid IT estates so that companies don’t lose sight of siloed information. This will cause an unnecessary impediment to progressive tools such as predictive analytics that can help businesses remain competitive.”

Despite infrastructure concerns, the report still found positive signs that organisations are investing in their data. 46% have made investments in data quality services and solutions to make their data more serviceable for machine learning and AI applications. 

Golombek explained, “Getting started with machine learning is more achievable than many think, and it is encouraging to see that organisations are starting to prioritise the quality of their data because this is crucial. Data, and the power to crunch it, is the nourishment these models need to learn and become more cognisant and the cloud offers project leaders the ability scale linearly from on-premise infrastructure and build competence for pilots or long- term programmes.”

The ‘Driving the Rise of AI and ML with Data’ report examines progress towards adopting machine learning and artificial intelligence, the strategies organisations are adopting and their technical implementation. It can be downloaded at

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