How AI could help businesses reduce data storage costs


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The amount of data managed by companies around the world continues to grow. According to one source, the total amount of data created, captured, copied, and consumed worldwide was approximately 64.2 zettabytes in 2020, or the equivalent of one trillion gigabytes. Unsurprisingly, companies are reporting that the cost of storing their data is also on the rise. In a 2018 Enterprise Storage Forum surveybusiness leaders said high operating costs, lack of storage capacity and aging equipment were among their top concerns.

Rising storage costs have pushed many companies to adopt cloud options, which offer the advantage of low entry costs. But with costs rising as more businesses go online – a Pepperdata report found that more than a third of enterprises had cloud services budget overruns of up to 40% – IT leaders are exploring alternatives.

On the cloud side, a nascent generation of startups is applying AI to the problem of cloud expense management. Vendors like Densify and Cast AI claim their AI-powered platforms can recommend the best storage configuration for an enterprise’s workloads considering various requirements. Other technology vendors have turned to on-premises systems, creating algorithms they believe can reduce storage costs, either with hardware suggestions or new file compression techniques.

“Data storage today suffers from several challenges: storage deployments often consist of a variety of different storage media such as memory, flash memory, hard drives and tape. Additionally, organizations are running multiple storage arrays based on access protocols…or based on workload criticality,” Arun Chandrasekaran, research vice president at Gartner, told VentureBeat via email. mail. “The use of AI has the potential to streamline data lifecycle management based on data criticality, performance, security and cost requirements.”

Cloud optimization

During the pandemic, the push to digitize operations has led a record number of companies to move to the cloud. According to a recent survey by O’Reilly, 90% of organizations were using some kind of cloud computing in 2021, while Flexera’s State of the Cloud report shows that 35% of enterprises spent more than $12 million on cloud operations in 2021.

The adoption trend has spawned startups developing AI-powered platforms designed to adjust usage to control spending. One is Densify, which analyzes workloads in private data centers, Amazon Web Services, Microsoft Azure, Google Cloud Platform, and IBM’s cloud offerings to determine how much CPU, RAM, and storage they have. need, then suggests ways to save. Densify can use the log data already available to begin optimization immediately. After that, the platform will continue to review cloud provider pricing changes, application needs, and new products to find where customers can further reduce spending.

“Usually in two to four weeks you see 50% of the savings,” CEO Gerry Smith told VentureBeat in a previous interview. “Depending on where the savings are, within two to four months, [you’ll get] 100% savings.

Cast AI, a competitor to Densify, is also leveraging AI to optimize cloud spend. Supporting major cloud service providers, the platform connects to existing clouds and generates a report to identify cost reduction opportunities.

“We have other models that use global datasets for predictions of market characteristics,” CEO Yuri Frayman told VentureBeat in October 2021. “For example, we train a global model to predict preemptions of instance by machine type, region, Availability Zone, and seasonality. This model is shared autonomously between all clients, and all data is used to recycle the model continuously. »

Onsite and compression

For businesses that haven’t migrated to the cloud — or whose data is spread across cloud and on-premises environments — there are solutions like Accenture’s Storage Optimization Analytics, which combines search and AI to understand enterprise content and automate data classification.

Accenture claims to reduce storage costs by detecting duplicate or near-duplicate content, helping clients move or archive the right data at the right time. Storage Optimization Analytics also automates migration to lower-cost storage and tracks storage savings, calculating overall return on investment (ROI).

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

An Nvidia AI model compressing videos.

AI is also playing an increasingly important role in file compression. For videos, music, and images, AI-based compression can deliver the same or nearly the same level of visual quality with fewer bits. Another advantage is that it is easier to upgrade, standardize and deploy new AI codecs compared to standard codecs, because models can be trained in a relatively short time and, importantly, do not require hardware. for special use.

Websites like and VanceAI Take advantage of templates to compress images without compromising quality or resolution. Qualcomm and Google have experimented with AI-based codecs for audio and video. And Alphabet-owned DeepMind has created an artificial intelligence system to compress videos on YouTube, reducing the average amount of data YouTube has to serve users by 4% with no noticeable loss in video quality.

To look forward

Gartner’s Chandrasekaran notes that adoption of AI technologies for data management, which fall under the category of “AIops,” remains quite low. (AIops platforms aim to improve IT by leveraging AI to analyze an organization’s data from tools and devices). But he adds that the pandemic has been a catalyst for adoption as organizations strive to automate faster to respond to “rapidly changing” circumstances.

Recent polls agree. According to Emergn, 87% of companies expect their investment in automation skills to increase over the next 12-26 months. And in a K2 2020 survey92% of business leaders said they consider process automation essential to success in the modern workplace.

“There is a lot of ‘AI washdown’ in the industry today, so verifying vendor claims and deploying a solution that delivers ROI can be frustrating. AIops requires a lot of integration,” said Chandrasekaran “For teams that are not skilled in architecting and maintaining complex data environments, a robust AIops deployment can become a pipe dream. There also needs to be a cultural shift, where organizations are ready to make decisions based on data.

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

“AI techniques can help optimize the placement of data on the right storage tiers, balancing performance and cost. Additionally, AI can contribute to better availability of data infrastructure, allowing enterprises to access data faster and build a reliable infrastructure,” Chandrasekaran added.

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