01 Unify Your Data Universe
Eliminate silos for frictionless access to data.
It’s not uncommon for large companies to have dozens, even hundreds, of different data repositories that make up their data estates. Each of these repositories is isolated and frequently owned by different business units across the organization. “This is where enterprises struggle,” Newton says. “Sometimes the data you want might be in a partner organization or somewhere else in the world.” What companies need is an open, flexible data fabric spanning the enterprise that ingests any data type into an open platform—allowing data scientists and engineers to access and analyze data across on-premises, cloud and edge environments. A data fabric—a consolidated design that, as Newton explains, “pulls silos into one global namespace”—can solve fragmentation with minimal disruption. HPE Ezmeral provides a data fabric that connects globally distributed data sources for enhanced, unified analytics without requiring a full data migration. “We have a number of customers with large amounts of data on-premises and in multiple public clouds,” says Matt Hausmann, group manager of HPE Ezmeral Go-To-Market (GTM). “HPE Ezmeral lets them unify all that data and then analyze it, wherever it’s located. One major automaker, for example, efficiently manages and analyzes millions of miles of vehicle test data at the edge using HPE Ezmeral.”
Data can stay where it’s at. If it’s in Hong Kong or New York, it doesn’t matter. I don’t need to move it to use it. That’s a big part of solving the data silo problem.”
Jason Newton Vice President, Global Marketing, HPE
02 Modernize Your Apps
03 Industrialize Your Analytics
Inject automation into your workflows.
04 Deliver A Unified Cloud Experience From Edge To Cloud
Avoid a data migration by utilizing a cloud that comes to you.
How To Do It
Ready to supercharge your company’s performance by unlocking data insights? Take a free HPE Ezmeral on-demand course—covering topics ranging from AI and ML to data security—and learn more about turning data into a competitive advantage for your company, without the IT overhaul.
Adopt cloud-native techniques for agility and faster insights.
Now that your data is unified, what’s next? Simplify data access across the company, empowering your workforce to leverage data without requiring changes to established access patterns or skill sets. “The challenge is: How do I give all users a great experience and connect them and the tools of their choice to the data sets they need?” says Newton. The answer? Move your analytics apps to the data—a more cost-effective, less arduous process than relocating data from myriad sources. Rather than bringing the data to your tools, allow apps and users to access data wherever it resides. The upshot is seamless interoperability between data and anyone seeking to extract its value.
We provide the freedom and flexibility to manage your infrastructure from a single pane of glass, deploy apps in minutes and analyze all of your data—which increases the productivity of data teams.”
Today, data analysis must be swift, airtight and accomplished at scale. This requires “industrializing” —or automating—a company’s artificial intelligence (AI), machine learning (ML) and other analytics tools to get the most out of your data as efficiently as possible. “To help you industrialize your analytics, our 100% open-source stack, built-in app store and growing ecosystem of certified independent software vendors (ISV) unshackle users from vendor lock-in,” Hausmann explains. The ability to mix and match open-source, validated ISVs or bring a tool of choice provides a single analytics lakehouse that bridges modern analytics techniques and traditional data warehouse capabilities. Paired with unified analytics and ML operations offerings, this allows users to create repeatable and reliable analytics pipelines across hybrid deployments, collaborate on models and speed time from development to production.
The whole concept of industrializing analytics is to pivot your company into an analytics factory to deliver faster time to insight.”
Matt Hausmann Group Manager, HPE Ezmeral GTM, HPE
The last step is to create a consistent cloud experience for data scientists, engineers and analysts—providing the freedom to orchestrate their data factory on-premises, in the cloud and at the edge. But the default answer here isn’t the public cloud, as most enterprises will continue to have a large on-premises estate. In fact, research from HPE finds that 71% of enterprises are planning to keep existing data platform workloads on-premises and modernize them to take advantage of cloud-native architecture. HPE now offers an alternative to the public cloud. With the HPE GreenLake edge-to-cloud platform, the new analytics cloud services based on HPE Ezmeral are built to be cloud-native and help avoid complex data migrations to the public cloud by providing an elastic, unified analytics platform for data and applications on-premises, at the edge and in public clouds. Benefits include a seamless experience for a variety of users, top-notch performance for hybrid environments, choice through open source and a reduction in total cost of ownership.
“These cloud capabilities accelerate the end-to-end analytics process. People want a consistent cloud experience where their data resides,” says Hausmann. “With HPE GreenLake for Analytics, we can bring that cloud experience to the data wherever it is.” With HPE doing the “heavy lifting” to simplify and modernize data analytics, Newton says, data experts can focus on what they’re best at: uncovering the answers hidden in data.
