UPDATED 17:18 EST / JULY 30 2020

BIG DATA

Industry experts address strategies and solutions for smart data lifecycle

Like an apple falling from a tree, data may have gravity, but its journey from creation to consumption to archive is hardly a straight, uninterrupted path.

In reality, the data lifecycle is a sequence of stages that are managed using different tools and technologies. The process can be complex and prone to problems, especially as data volumes climb to nearly unfathomable levels.

Ultimately, the data lifecycle journey must begin with knowledge.

“The number one problem that we are trying to solve for our customers is to help them understand what they have,” said Lester Waters (pictured, second from left), chief technology officer of Io-Tahoe LLC. “If they don’t understand what they have in terms of their data, they can’t manage it, they can’t control it, they can’t monitor it, they can’t ensure compliance. Finding all that you can about the data that you have and building a catalog that can be readily consumed by the entire business is what we do.”

Waters spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, as part of the recent Smart Data Lifecycle CrowdChat. He was joined by Patrick Smith (pictured, second from right), field chief technology officer of Pure Storage Inc., and Ezat Dayeh (pictured, right), systems engineering manager for the U.K. and Ireland at Cohesity Inc. Adam Worthington, co-founder and chief technology officer at Ethos Technology Ltd., spoke with Vellante in a separate interview. They discussed the value of metadata in communicating across platforms, the need for automation, dealing with organizational silos, the role of the cloud, and achieving nirvana in a data-driven economy. (* Disclosure below.)

Governance and agility

A critical need to understand enterprise data has placed companies in a position of applying smart technology to help with governance, agility and performance at each stage of the lifecycle. This is where metadata, encoded information that describes the data itself, becomes especially important in the process of discovery and management.

Perhaps more significantly, data management companies are looking at metadata as a way to work together in delivering solutions for customers.

“Imagine for my data lifecycle I can communicate with the backup system for Cohesity and find out the last time that data was backed up or where it’s backed up to,” Waters explained. “I can exchange data with Pure Storage and find out what tier it’s on. Is the data at the right tier commensurate with its use level? Being able to share metadata across systems is the direction that we’re going in.”

Building in processes that use metadata goes hand-in-hand with another key element: automation. The sheer volume of data coupled with growing demands for regulatory compliance have increased pressure on enterprises to find automated solutions.

This need for data automation was highlighted in a recent survey, which found that companies continue to struggle with meeting compliance standards set by the California Consumer Privacy Act. Barely a third of respondents had fully automated customer access to personal data.

“Data volume is just so massive now that you can’t effectively manage, understand or catalog your data without automation,” Smith said. “Once you understand the value of the data, then you can work out where the data needs to be at any point in time.”

Unwieldy backup

The process of data backup, a common procedure for most information-technology organizations, can itself lead to data sprawl and clog an enterprise’s ability to derive value from the information it has. Ethos Technology confronted this issue with one customer that had allowed its data backup process to become unwieldy and burdensome for the company.

“It’s a huge organization with literally petabytes of data that were serviced in their backup and archive,” Worthington said. “What they had was not just reams of data; they had five different backup applications that were dependent on what area of infrastructure they were backing up to. Through a consolidated approach that we recommended, they were able to significantly reduce complexity and reduce the amount of time that it took them.”

Another key element of the data lifecycle for enterprises is to unify data fragmentation across silos. Purpose-built backup appliances often contribute to this problem by creating data silos within organizations. Companies such as Pure Storage offer Flash-to-Flash-to-Cloud backup models to help simplify IT operations.

“If you look at the infrastructure that supports many peoples’ data landscapes today, for primarily legacy reasons, the infrastructure itself is siloed,” Smith explained. “Historically, you had specific fitness to purpose for different data requirements. That’s one of the challenges that we tackled head on at Pure with FlashBlade technology and the concept of the data hub, a platform that can deliver in different characteristics for different workloads.”

Cloud native vs. hybrid

Pure’s “flash-to-cloud” approach is part of an ongoing conversation around the role of cloud in smart data solutions. Migration of data-heavy workloads to the cloud continues to be an ongoing trend. Yet, some industry leaders are questioning whether a wholesale shift makes sense.

Dayeh draws an analogy between hotels and houses when evaluating cloud versus on-premises options for data.

“I’ve seen customers go into the cloud very quickly, and recently they’re starting to remove workloads from the cloud,” Dayeh said. “Why do we all live in houses and not hotels? The simple answer is that it’s cheaper. The cloud is great for many things, but it can work out costlier for certain applications.”

However, the evolution of smart data management also includes the flexibility to run business workloads in hybrid environments, combining cloud and on-premises platforms. Results from a survey released in late July show that 72% of respondents characterized their cloud strategy as hybrid-first.

“It reinforces the importance of hybrid and multicloud environments because it gives you that flexibility, the optimal environment to run your business workloads, and that’s what it’s all about,” Smith said. “We started off entirely cloud native, but now we use public cloud for compute and we use our own technology, the end of our high-performance link, to support our data platform. So, we have the best of both worlds, and I think that’s where a lot of our customers are trying to get to.”

One recent forecast shows that an estimated 41 billion connected internet of things devices will generate 80 times the amount of data that comprised all global internet traffic in 2016. That will demand tools which allow enterprises to take full advantage of this massive amount of information, from creation to consumption to archive.

“The data economy is all about how a business can leverage their data to compete in this new world order that we are now in,” Waters said. “Nirvana from my perspective is really getting to a point where you’ve consolidated your data, you’ve broken down the silos, and you have a virtually self-service environment by which the business can consume and build upon their data.”

Watch the complete video discussion below as part of theCUBE’s coverage of the Smart Data Lifecycle CrowdChat(* Disclosure: TheCUBE is a paid media partner for Io-Tahoe LLC. Neither Io-Tahoe, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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