We Need a New Approach to Beat Data Management Complexity

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Not a day goes by that we don’t come across a business article discussing the benefits of data and its ability to transform organizations. We read about a new world of possibilities, with data ushering in new customer experiences and powering the next wave of applications that will connect data to insights — and to outcomes. But the disparity between where organizations are today on their transformation journeys, and where they want to go, is growing, and it's a reason for concern.

What stands in the way of innovation is complexity — a complexity that spans people, process, and technology, and it’s rooted in how data and data infrastructure are managed. What does that look like? A recent ESG study found that among organizations, 93% of IT decision makers see storage and data management complexity impeding digital transformation. As an example of that overhead, on average these organizations rely on 23 different data management tools! That’s an overwhelming amount of disparate hardware and software (not to mention the people silos) needed to manage the lifecycle of data and data infrastructure — from how data is accessed, protected, governed, and analyzed to how infrastructure is deployed, provisioned, upgraded, and mobilized.

Complexity impacts everyone — and is only growing

Organizations have lived with this complexity for years — and yet it’s precisely what stands in the way of transformation. How so? It comes down to today’s approach to data and infrastructure management and the way that impacts everyone. For starters, think about storage and the headaches that IT deals with every day: countless hours spent tuning, maintaining, and upgrading storage across fleets. Tradeoffs must be made between resiliency, efficiency, and performance. Provisioning is manual and burdened with guesswork. Cloud seems like a potential answer, but data and apps are needed everywhere.

And the impact of complexity reaches beyond just IT. Data innovators — those who turn bits and bytes into new apps and insights — can’t get access to data fast enough. Manual processes inhibit data utilization and slow down time-to-value. Data managers are challenged to both streamline data access and protect that same data within an ever-intensifying threat landscape.

So, what can be done? Today, by capitalizing on the power of data, cloud, and AI, we can reimagine the data experience.

A new paradigm for data and infrastructure

Let’s get to the essentials. We need an architecture that embraces data, cloud, and AI to create a new data experience through data-centric policies and automation, a cloud operational model, and AI-driven insights and intelligence. Here are the essentials of a modern approach to data and infrastructure management.

Develop data-centric policies and automation

Data has a continuous lifecycle spanning test/dev, production, protection, and analytics. It needs to be managed holistically, from creation to deletion. Software that can only manage individual parts of the lifecycle is inefficient and creates visibility gaps. Instead, we want to apply holistic, data-centric policies and automation that collapse silos and unify workflows across the data lifecycle. That means ensuring that policies that manage how data is stored, accessed, protected, and mobilized — even how applications are provisioned — are data-centric and automated.

Leverage a cloud operational model

Cloud has set the standard for agility: the cloud operational model enables line of business (LOB) owners and developers to build and deploy new applications, services, and projects faster than ever before. This renders underlying data infrastructure invisible and shifts operations to be app, not infrastructure, centric. Extending that idea further, organizations should leverage the cloud operational experience wherever their data and app workloads live, from edge to cloud.

Part of the transformation journey involves evolving IT to an “as a service” model. Based on the cloud operational experience, as a service infrastructure radically simplifies and automates management — freeing up staff to work on higher-value initiatives and delivering the self-service agility that LOB owners and developers need to go faster.

Harness AI-driven insights and intelligence

AI is a critical dimension in any modern IT architecture. It continues to transform every industry with unprecedented intelligence, creating autonomous operations across manufacturing, transportation, and healthcare, to name a few. Just as we rely on Google Maps to see ahead and reroute us if needed, businesses need AI to be deeply integrated into data operations. Imagine being told you could avoid a disruption by making a network setting change, or improve app performance by rebalancing workloads and resources in a specific way, or provision applications instantly across your entire fleet without any planning or calcs. That’s the power of AI-driven insights and intelligence.

With this new paradigm for data, we’ll transform the data experience across organizations — creating value for everyone from IT managers to data innovators. Instead of tuning and maintaining infrastructure, IT managers simply deploy cloud services with instant application provisioning. Instead of waiting days to access data, developers and data scientists get streamlined access on-demand. Instead of worrying about threats to data, data managers can set protection policies with a single click wherever data lives.

HPE is taking the lead in redefining data and infrastructure management with just such a new vision for data. Bringing together cloud data services, cloud-native infrastructure, and AI-driven intelligence — all delivered as a service — HPE GreenLake uniquely provides a single, edge-to-cloud platform to connect applications to infrastructure, innovators to data, and automation to policies in a seamless, unified cloud operational experience wherever data lives.

For IT leaders, there’s finally an answer to the ever-growing challenges of complexity. With the HPE GreenLake edge-to-cloud platform, you can accelerate your data-first modernization by collapsing silos across people, process, and technology and unleashing data, agility, and innovation for your organization.

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About Sandeep Singh

sandeepprofile
Sandeep is Vice President of Storage Marketing at HPE. He is a 15-year veteran of the storage industry with first-hand experience in driving innovation in data storage. Sandeep joined HPE from Pure Storage, where he led product marketing from pre-IPO $100M run rate to a public company with greater than $1B in revenue. Prior to Pure, Sandeep led product management & strategy for 3PAR from pre-revenue to greater than $1B in revenue – including four-year tenure at HP post-3PAR acquisition. Sandeep holds a bachelor’s degree in Computer Engineering from UC, San Diego and an MBA from Haas School of Business at UC Berkeley.

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