Accelerate predictive analytics software with flash technology

Meeting requirements

According to Vincent Hsu, IBM's CTO for storage, there are three basic requirements storage must meet to effectively support analytical workloads: compelling data economics, enterprise resiliency and easy infrastructure integration.

"Put simply, faster response times can yield more business agility and quicker time to value from analytics, and more data analyzed at once means more potential value streams," Hsu said.

There's a competitive race today to use predictive analytics software in many forms, including machine learning and deep learning applied to operational optimization.

"By becoming predictive at increasing operational speeds, organizations can not only find marked improvement in existing business processes, but exploit disruptive new approaches to their markets," Matchett said. "We've seen predictive analytics evolve from offline, small data scoring into massive web-scale, big data, real-time decision-making."

Predictive analytics software is not just about analysis, but gaining the ability to respond -- rather than react -- to rapidly changing market conditions.

"Since actions based on the results are the whole point, faster, smarter and more relevant results win the day and, as a result, flash wins out," said Donna Taylor, head of consulting firm Taylor & Associates and former Gartner analyst.

Matchett noted that organizations can add flash technology to almost any modern array in the form of cache or as a fast storage tier.

"We also see some innovation in having storage architectures 'link up' server-side flash as a virtual local performance tier of persistence," he said.

Turning data into insights

The key obstacle many predictive analytics software users face is limited file-access speed.

"While the raw storage capacity of legacy [or] traditional storage has increased dramatically in recent years, the rate at which the data can be accessed and served has remained relatively flat," said Sam Ruchlewicz, director of digital strategy and data analytics at Warschawski, a marketing communications agency based in Baltimore that uses predictive analytics software to study consumer trends and behaviors.

"As the sheer volume of customer data continues to grow, more predictive analytics applications are moving to flash storage to efficiently and effectively access actionable information," he said.

Ruchlewicz noted that one of the biggest challenges in his field is making sense of terabytes -- or even petabytes -- of customer data in real time, then using that insight to deliver a better customer experience at relevant touch points.

"To accomplish that goal, the predictive analytics application [or] algorithm must query the database for the requisite information, process it and provide the result to the next component of your marketing technology stack," he said. Flash technology is the key to making this process fast and efficient.

As they look to accelerate their predictive analytics capabilities, organizations must carefully examine where a flash technology investment can make the most sense.

"Storage-side flash tends to be shared widely, but is probably the most expensive," Matchett said. "Server-side flash, such as NVMe, can provide a huge boost to applications that can make use of it locally, but might be quite a large investment to make across a large big data cluster."

Matchett noted that flash storage prices will continue to fall, even as capacities increase.

"What is interesting is that we also see some possible new tiers of faster persistence coming with ReRAM MRAM and the like," he said.

For now, many predictive analytics software users rely on a combination of storage media types, including HDDs, tape and flash technology.

"This is nothing new; however, companies looking to squeeze additional value from dense data sets will increasingly adopt flash technology in order to reap the benefits of faster seek and processing speeds," Taylor said.

The essential attribute most flash customers are looking for, according to Hsu, is data agility -- the automated, policy-driven reallocation of data to and from a storage medium without a lot of human intervention or time-consuming, expensive steps.

"It is in this state of data agility where flash really shines and paves the way for artificial intelligence and machine learning," Hsu said.

Taylor urged predictive analytics software users who are planning a full or partial transition to flash technology to thoroughly research the market. "Otherwise, they risk being at the mercy of a salesman's skewed sales pitch," she says.

Flash forward

Ruchlewicz said he would advise any organization considering an infrastructure investment designed to support an analytics initiative to seriously think about using flash storage, noting that most predictive models request data faster than a legacy system can provide it.

"Even if the organization's data set is within more reasonable bounds, flash is the superior alternative and the system of the future," he noted.

Hsu concurred. "Data is the most valuable commodity organizations can lay claim to, and any organization that considers speed and insight as a competitive advantage can benefit from flash storage for predictive analytics," he said.