How AI could help enterprises to reduce data storage costs

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The amount of data powered by the world’s enterprises is increasing. According to one source, the total amount of data created, captured, copied and used globally in 2020 was approximately 64.2 zetabytes – equivalent to one trillion gigabytes. Surprisingly, companies report that the cost of storing their data is also increasing. In a survey of the 2018 Enterprise Storage Forum, business leaders stated that high cost of operation, lack of storage capacity and aging equipment were among their top concerns.

Rising storage costs have forced many companies to embrace cloud options, with the advantage of lower entry costs. But as more businesses go online, costs are rising – the PaperData report found that more than a third of companies’ cloud service budgets have increased by 40% – and IT leaders are exploring alternatives.

On the cloud side, a new crop of startups is implementing AI for the problem of cloud cost management. Vendors such as Densify and Cast AI claim that their AI-powered platforms can recommend optimal storage configurations for companies’ workloads taking into account different requirements. Other technology providers have focused their attention on on-premises systems, creating algorithms that they claim can reduce storage costs with hardware instructions or new file compression techniques.

“Data storage today faces many challenges: storage deployments are often made up of different storage media such as memory, flash, disk drives and tapes. In addition, organizations run multiple storage arrays based on access protocols … or depending on the complexity of the workload, “Gartner Research VP Arun Chandrasekhar told VentureBeat via email.” Is. “

Cloud optimization

During the epidemic, a record number of companies moved to the cloud due to the pressure to digitize operations. According to a recent survey by O’Reilly, 90% of organizations were using some form of cloud computing in 2021, while Flexera’s State of the Cloud report shows that 35% of companies spent more than $ 12 million on cloud operations in 2021.

The adoption trend has led to the development of AI-powered platforms for startups designed to adjust access to governance costs. One is Densify, which analyzes workloads on cloud offerings from private data centers, Amazon Web Services, Microsoft Azure, Google Cloud Platform and IBM to determine how much CPU, RAM and storage they need – then suggests ways to save. Densify can use already available log data to start optimizing immediately. After that, the platform will continue to review cloud providers’ price changes, application requirements and new products so that customers can further reduce costs.

“Usually in two to four weeks, you have 50% savings,” CEO Gary Smith told VentureBeat in an earlier interview. “Depending on where the savings are, in another two to four months, [you’ll get] 100% savings. “

Cast AI, a density competitor, similarly takes advantage of AI to optimize cloud costs. Supporting major cloud service providers, the platform connects to existing clouds and generates a report to identify cost-saving opportunities.

CEO Yuri Framan told VentureBeat in October 2021, “We have other models that use global datasets for market characterization forecasts. This model is autonomously shared with all customers and all data is used to continuously train the model.

On-premises and compression

Companies that have not moved to the cloud – or whose data is spread across cloud and on-premises environments – have solutions like Accenture’s Storage Optimization Analytics, which combine search and AI to understand enterprise content and automate data classification.

Accenture claims that it reduces storage costs by finding duplicate or nearby duplicate content, helping customers move or archive the right data at the right time. Storage optimization analytics also automates transfers to low-cost storage and tracks storage savings, calculating gross return on investment (ROI).

IT provider Rahi Systems offers a similar service called Pure1 Meta, which uses AI models to predict capabilities and performance and to advise on workload deployment and optimization. Pure1 Meta can run simulations for specific workloads, generating answers to capacity planning questions while apparently helping to increase resource utilization.

Nvidia AI model compressed videos.

AI is also increasingly playing a role in file compression. For videos, music, and images, AI-based compression can provide the same – or near the same level of visual quality with fewer bits. Another advantage is that the new AI codecs are easier to upgrade, standardize, and deploy than the standard codecs, as the models can be trained in less time and – relatively – do not require special purpose hardware.

Websites like and VanceAI take advantage of the model to compress images without compromising quality or resolution. Qualcomm and Google have experimented with AI-powered codecs for both audio and video. And DeepMind, owned by Alphabet, has created an AI system for compressing videos on YouTube, which reduces the average amount of data YouTube needs to stream users to 4% without significantly compromising video quality.

Looking to the future

Gartner’s Chandrasekharan notes that the adoption of AI technology for data management, which falls into the category of “AIops”, is very low. (The purpose of the AIops platform is to enhance IT by taking advantage of AI to analyze data from organizations’ tools and devices). But he adds that the epidemic has been a catalyst for adoption as organizations seek to automate quickly in response to “rapidly changing” circumstances.

Recent surveys agree. According to Emergen, 87% of companies expect their investment in automation skills to grow over the next 12 to 26 months. And in the 2020 K2 poll, 92% of business leaders said they value process automation for success in the modern workplace.

“There is a lot of AI washing in the industry today. As such, verifying vendor claims and using solutions that deliver ROI can be frustrating. AIops need a lot of integration, “said Chandrasekhar. “For teams that are not proficient in the architecture and maintenance of complex data environments, strong AIops deployment can be a dream. There is also a need for cultural change, where organizations are willing to make data-driven decisions.

Looking ahead, Chandrasekaran expects to see more “versatile” AI-powered storage management solutions out of the products already available in the market. These solutions could enable more intelligent automation and remediation workflows through the use of AI, he believes.

“AI technologies can help optimize data placement at appropriate storage levels – by balancing performance and cost. In addition, AI can help improve the availability of data infrastructure, enabling businesses to access data faster and build a more reliable infrastructure, “added Chandrasekhar.

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