Data ROI Today and Tomorrow: Is your Data strategy ready for AI
- July 24, 2019
In the year 500 BC, Heraclitus the Greek philosopher said, “There is nothing permanent except change,” maintaining that change is the only reality in nature. Little did he know that more than 2,000 years later, the rate of change would increase exponentially in frequency, velocity and volume. In business today, the speed of change is creating all new challenges for IT organizations to effectively compete, maintain industry and customer relevance, drive innovation and ultimately grow their organizations.
Data demands are at the forefront of these challenges as data continues to grow in size and scope. Adding to that are the expectations from end-users on being able to immediately access insights contained within that data to help them make smarter business moves, drive more efficient operations and keep customers happy, in turn helping to increase profits.
As data becomes the new currency driving accelerated levels of innovation, the opportunity to future-proof your data center becomes even more relevant to ensure it is driving ROI for you now and further down the line. What’s required is a modern data storage architecture that is able to support your evolving analytics needs and designed to shorten time to insights, while simplifying complex data pipelines.
While organizations across multiple vertical markets have different ideas about how to utilize their data and the applications they need to run their businesses, the building blocks needed to future proof the data center remain the same:
The Data and Storage Architecture is even more critical for AI
These essential elements are necessary to help you better manage and take advantage of your vastly growing data sets. As Moor Insights & Strategy details in their new paper, Enterprise Machine & Deep Learning with Intelligent Storage, your data architecture decisions become even more critical as your business delves into deep learning and machine learning at scale.
“While discussions of machine learning and deep learning naturally gravitate towards compute, it’s clear that these solutions force new ways of thinking about data,” the firm notes in its “Enterprise Machine & Deep Learning with Intelligent Storage” paper. “Deep learning requires thinking differently about how data is managed, analyzed and stored.”
The main takeaway from the paper is that serving up data for machine learning and deep learning is very different from any other enterprise workload, Moor Insights & Strategy says. And here’s some of what you need under the hood, according to the firm: “Managing data for deep learning requires deploying solutions that are built for high concurrency and multi-dimensional performance at scale with tiering across a single namespace and simple management through a consistent set of tools.”
Dell EMC has it all covered
As the rate of change increases, it’s imperative that you’re proactive in ensuring your data center is ready to stand the test of time. The solutions in the Dell EMC storage portfolio address all of the needs outlined in the Moor Insights & Strategy report.
“The Dell EMC Isilon family provides a solid base from which to deliver storage capabilities in supporting the full life-cycle of enterprise deep learning,” Moor Insights & Strategy says. “This follows the workflow from training, learning, deployment and, ultimately, to long-term archival needs.”
These innovative solutions include the Dell EMC Isilon storage system with the OneFS operating system, the Dell EMC PowerMax, and an expanding portfolio of Isilon based Ready Solutions and Reference Architectures built to simplify AI and deliver faster, deeper insights. By offering new ways to store, manage, protect and use data at scale, we can help you continue to evolve and pivot to squeeze out every last cent of value from your data.
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