Why Intelligent Data Platforms Are the Future

The big difference in the last few years is that AI and modern application workloads are adding pressure to an already complex data environment. Customers need efficient management of their data to feed their AI factories. This further complicates the data management challenges, just bigger, faster, and more critical.

TA: How have expectations changed for IT teams when it comes to managing data infrastructure? We’ve seen shrinking ops teams, with limited budget and resources. Are we asking too much of IT?

RJ: Yes, we probably are! Back in the day, IT had dedicated specialists focused on specific management of IT areas such as storage, networking, or servers. That model doesn’t always exist anymore. A lot of the experts have moved on, and today’s teams are now running with more generalized IT skills. Today’s professionals don’t want to be storage experts; they have more broad IT skills. 

At the same time, the budgets for IT infrastructure management have shrunk. The expectations haven’t changed—organizations still want high performance, capacity, and reliability—but they have limited staff to accomplish this. And still deliver the same output. The infrastructure they’re managing hasn’t become any simpler, so the systems themselves must get smarter.  That’s why we need to build data systems that are more attuned to the IT generalist. 

TA: It’s the familiar battle cry of “do more with less.” But it sounds like that might be possible in the context of data management, with the right platform? 

RJ: It’s possible—but, yes, only if we build data management platforms that are more intelligent, like I mentioned. The systems need to be easier to manage, smarter, and more intuitive, which can help bridge IT skills gaps and smaller budgets. You need automated data management processes that’s smart enough to observe what’s happening across the data infrastructure and help you to make the right decisions. That’s where we’re seeing success, with customers consolidating tools and starting to automate operations through data management platforms that have actionable intelligence built in.

TA: Data visibility across environments seems like one opportunity to utilize smarter tools, and it’s a big priority for many organizations. Where are most teams still struggling with this? 

RJ: It starts with knowing what you have. A lot of customers don’t know all of the data that they have or where it is. They’ve got heterogeneous data system environments—some data in the cloud, some on-prem, some that hasn’t been accessed in years, while most that’s being accessed every day. You can’t manage or tune anything unless you have the right data visibility first. All the data infrastructure elements—storage systems, servers, switches—they’re all outputting telemetry data. But there’s no easy way to go through all of it and figure out what to tune. 

So, the first step is capturing all that telemetry data and correlating it so you can better understand and analyze it. AIOps is all about using AI data-driven insights to help you better manage operations. It’s about taking the telemetry data coming from your systems and making sense of it using AI. Instead of someone calling to say, “Oracle is slow,” AIOps can help analyze your data environment to identify issues and provide recommendations. It’s not just about collecting data, it’s about using AI to understand it, analyze it, and act on it.

TA: How does AI Ops help better manage the data? Especially in a hybrid cloud environment, where data is spread across different platforms and organizations struggle with multiple data silos? 

RJ: Part of the problem is that different data solutions have different characteristics—structured data like databases, unstructured data like video files—and that leads to requiring the use of different storage data types. The solution is to use a common data platform that can work across those data silos to enable a single data plane, where you can support different types of data, structured or unstructured, with one data platform. You can also virtualize storage—for example, you can take virtual Hitachi storage systems or third-party storage arrays and provide common data services, so it behaves like parts of the same data infrastructure. On top of that, we can apply unified data management with VSP 360, so there is one common control plane to manage all these shared data services.

Once you do that, these data platforms will enable you to control, observe, and govern data with one unified data management approach. That’s how you can eliminate data silos.

TA: Let’s talk about data governance. It often gets lumped in with compliance checklists. Where do you see its strategic potential and its competitive advantage?

RJ: Governance is about more than just compliance. It’s about ensuring your data is protected, accessible, and usable. That includes protecting personally identifiable information (PII). With the right tools, you can scan systems for sensitive data like social security numbers and other PII, and make sure it’s secured, while also staying compliant with laws and regulations.

The real advantage is integration. If you can govern from the same place, you manage and observe your infrastructure, you’re saving time and reducing risk. It’s the difference between proactive insight and reactive damage control.

TA: We’ve talked about how AI Ops can help organizations manage data, but what about organizations trying to build their own GenAI applications? How critical is data management for businesses wanting to implement AI? 

RJ: If you’re building new AI applications, you also need to optimize your data management operations—whether you want to or not. Data scientists need clean data pipelines to train AI models but they often aren’t data infrastructure experts as well. Without the right data visibility and quality, AI suffers. Poor data quality leads to inaccurate results, wasted compute cycles, missed insights, and potential regulatory risk. Garbage in, garbage out. 

Managed properly, data can become a strategic asset. That means classifying, cleaning, and optimizing it throughout its lifecycle.

TA: Thank you so much for your time and insights! Final question: Where are you seeing the most innovation in data management today?

RJ: We’re seeing innovation around three big themes: automation, integration, and data intelligence. Customers want to simplify operations and get more value from their data. That’s driving demand for data platforms that unify management, observability, and governance. The future of data management is about reducing complexity, so customers can focus on creating new data solutions with real business outcomes, not spending their time managing the data infrastructure.