Over the past 16 months, we\u2019ve covered the new age of data from various perspectives. First, the explosion of enterprise data itself \u2014 particularly unstructured data \u2014 and how that\u2019s radically changed the way companies must evolve their data strategies. We also examined the ideal data infrastructure architectures for analyzing all that data and delivering business value. Finally, we updated readers on current trends in enterprise analytics, including cloud strategies and the move to open source analytics solutions.\nIt\u2019s a lot to digest, but there\u2019s one more data analytics topic to address: the essentials for building out your own workloads. Let\u2019s dig in.\nIf your situation is like many enterprises across industries today, your data is in disarray \u2014 which is to say, it\u2019s likely spread across your environment in a mix of storage infrastructures (on-premises, cloud, edge, data lakes, data warehouses), each one of a different vintage and using different protocols, APIs, and so on. Some of your data is currently unreachable, and much of it is not subject to the analysis that\u2019s going to drive future insights and business transformation. Your objective now \u2014 challenging as it may sound \u2014 is to pull off data-centric modernization in a landscape of siloed data and multi-generational data infrastructure.\nSo, from such a starting point, how do we build out your modern analytics?\n1. Solve for object storage on-prem\nWhen considering your strategy for dealing with new, massive data sets, object storage is increasingly popular because it provides crucial advantages. No longer simply a cheap and deep archive for data, object storage is designed for \u2014 and easily \u200b\u200bhandles \u2014 the large volumes of data required to build, train, and manage analytic models. And, just as important, object storage is highly scalable, making it perfect for the large and unpredictable data volumes that analytics workloads must contend with.\nThere\u2019s one other attribute that makes object storage unique: it\u2019s industry-standard protocol, S3, is the lingua franca of the cloud, so you don\u2019t have to be an expert to use it. Nor do the data engineers, data architects, and data scientists on your teams, because they\u2019re already familiar with it. With an API-centric model that\u2019s easy to use, object storage already aligns with your modernization efforts.\nYou\u2019re most likely using object storage-based resources in the cloud. To bring greater agility and flexibility to your entire environment, it makes sense to enable an on-prem object storage solution. For that, you\u2019ll need management software with the flexibility and consistency to deliver seamless on-prem and cloud operations, according to your business needs. By unifying your data operations in this way, you\u2019re not just eliminating silos, you\u2019re empowering teams enterprise-wide \u2014 from business intelligence analysts to SQL and Spark users, to machine learning data scientists \u2014 to accelerate. Ultimately that means faster time to value.\n2. Leave yourself open to a wide variety of tools\nOpen source solutions like Spark, Delta Lake, Livy, and Hive can add powerful analytic tools to your organization while also removing the risks of lock-in. Look for data platforms that offer integrations with the leading open source tools alongside a marketplace of partners who can further expand your options.\n3. Integrating Kubernetes is a must\nAlthough certain large, scale-out environments will inevitably remain on bare metal, container environments are now ubiquitous in analytics deployments. Highly portable and efficient, containers deliver unprecedented agility and speed across clouds. Organizations transforming today need to capitalize on the move to containers by leveraging an orchestrated Kubernetes environment that automates the provisioning and management of applications.\n4. Leverage the flexibility of hybrid cloud\nAs you modernize, hybrid cloud becomes almost unavoidable. Whether you\u2019ve made the move to hybrid cloud yet or not, you\u2019ve probably already seen how the ability to deploy and migrate workloads across on-prem and public cloud based on performance, security, cost, and more can be invaluable. Now, to lock in those advantages, you need to ensure seamless app and data mobility across clouds via modern, edge-to-cloud data services capable of optimizing each and every workload.\nThe bottom line \nOnce you\u2019ve addressed each of these considerations, you\u2019re ready to build the analytics solution that will let you see around corners, drive innovation, and gain the advantages to power success into the future. With its industry-leading, as-a-service infrastructure solutions, HPE is already helping thousands of customers realize their goals.\nFind out how HPE can help get you started on your journey.\n____________________________________\nAbout Matt Miller\n\nMatt leads Solution Marketing for the HPE Storage business, covering such areas as file and object storage, scale-out storage, virtualization and containers, and cloud-native technologies.\u00a0 \u00a0Matt has a nearly a 20-year tenure in the storage industry, and came to HPE through the acquisition of Nimble Storage in 2017.\u00a0 At Nimble, Matt held product and solutions marketing roles where, in part, he grew the converged infrastructure business to over $100M and also led marketing for Nimble\u2019s ground-breaking AIOps engine, InfoSight.\u00a0 Matt has also worked for industry innovators such as NetApp, Sun Microsystems, Veritas Software and Compaq.\u00a0 He holds a Bachelor\u2019s degree in Business Management from Marist College, and an MBA from Vanderbilt University.\u00a0 Matt resides in the San Francisco Bay Area with his wife and two daughters.\u00a0 Connect with Matt on\u00a0LinkedIn\u00a0and\u00a0Twitter!