Federal agencies are modernizing aggressively, driving the addition of new systems and capabilities and creating increasingly diverse hybrid cloud ecosystems. While such modernization is necessary to keep up with growing service mandates and citizen expectations, the complexity that arises from these hybrid cloud architectures poses significant challenges in orchestrating and monitoring government IT systems.

To solve this conundrum, federal IT leaders must lean into artificial intelligence and automation to better manage their complex IT environments. When supported by a strong data management foundation, this combination can deliver enhanced service-level visibility and control for government IT teams in charge of ever-changing hybrid cloud architectures.

Hybrid cloud brings challenges of complexity and scale

As government networks load up on new data and applications, gaining visibility over modern IT estates has become more difficult than ever. Rather than adopt a single cloud service from a single cloud provider, agencies are embracing a wide range of cloud vendors and approaches. This can leave teams, who may already be understaffed and swimming in technical debt, siloed and struggling further to manage a workload-intensive mix of legacy and modern applications and infrastructure.

This dramatic proliferation of operational complexity is fueled by massive increases in the volume, variety and velocity of data to be managed. Additionally, IT platforms are often not accessible, understandable or usable for many user-level government workers who need to collaborate on them. The picture is further complicated by the fact that not all workloads are moving to the cloud and by the persistence of legacy monitoring tools that aren’t able to keep up with the variety and velocity of data across hybrid cloud architectures.

All these factors contribute to an unsustainable scenario of outdated tools and disjointed processes that stifles IT’s ability to respond to spiraling complexity and keep up with evolving agency and end user expectations. Fortunately, government IT teams can overcome these obstacles by making strategic use of both AI and automation to progress towards a state of autonomic IT and bring more visibility and control to their hybrid cloud architectures.

Overcoming hybrid cloud complexity with AI plus automation

To make sense of the current state of hybrid cloud complexity and better meet key mission objectives, federal IT teams must opt for a modern approach to ITOps that combines AI and automation to create a more unified service view across the entire hybrid cloud universe. This includes all data center, public cloud – software-as-a-service, infrastructure-as-a-service and platform-as-a-service — and private cloud environments.

The combination of AI and automation is crucial to driving observability across each of these environments, applying machine learning and scalable process optimization throughout all hybrid infrastructure data and systems. This empowers staff to perfect and then automate routine operational tasks, such as collecting diagnostic data, exchanging real-time operational data between systems and platforms, executing ticketing, remediation workflows and more.

The most successful deployments combine a wide range of data across environments to establish a real-time operational data lake. This makes it possible for IT teams to analyze and act on the data at “cloud scale” while applying a rich set of analytical techniques to add business service context and meaning to the data – with multi-directional workflows for both proactive and responsive actions.

Facilitating AI and automation with stronger data management techniques

While there is no single blueprint to follow for applying AI and automation for more alignment and orchestration of agencies’ hybrid cloud environments, the most successful efforts make sure to prioritize the underlying integrity of data. The right data management foundation will allow AI to properly manage, model and analyze operations, and this foundation is also essential to optimize and scale processes with automation.

In particular, federal IT teams should pursue three essential data-related priorities to support the journey to complete visibility and autonomous IT operations. To begin with, data must be of high fidelity, meaning it’s critical to collect the right types of data from the right sources in order to accurately reflect the state of what’s happening with an agency’s IT and business services at any given time. In addition, the cleaning, analyzing and acting on data must happen in real-time – ideally via automated processes and closed-loop decision making to enable action quickly without the need for a human analyst to be involved.

Throughout, data must be thoroughly contextualized, with all metadata and asset dependencies clearly defined through a service oriented view that enhances the ability to understand operational patterns and identify anomalies or performance issues. The right platform for AI and automation will include capabilities for managing data in these ways, enabling teams to cut through the noise and quickly establish the impact and root causes of issues. This, in turn, sets the broader stage for fundamental IT and agency transformation toward stronger agility, speed and growth.

As governments become increasingly digitized, many agencies struggle to manage their integrated hybrid-cloud environments. Fortunately, the right combination of AI and automation founded on the right data management techniques can bring more visibility and control to these environments. As a result, federal IT teams can conduct faster root cause analysis, reduce downtime, optimize IT investments, and provide a more stable foundation to support broader agency modernization efforts as technology continues to advance.

Lee Koepping is senior director for global sales engineering at ScienceLogic.

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