Slowdowns caused by system disruption and complexities in your IT environment are more than an operational headache. They can have a direct impact on the bottom line. While it’s enormously important to make IT systems more efficient and give time back to the organization, it’s just as important to recognize the value of that time and understand the best ways to allocate it between workers, apps, and infrastructure. It comes down to how much time individual stakeholders spend on reactive vs. proactive tasks.
That’s where AI and the cloud operational experience come in. AI-powered operations – or AIOps – can change the value of time for your business.
Reactive time equates to lost time
For IT organizations that constantly operate in a reactive mode, infrastructure and application management can feel like sailing upwind, dragging an anchor behind the boat while you attend to urgent issues.
Application and infrastructure downtime routinely brings end user productivity to a standstill and impedes your ability to support strategic initiatives. All of the time your team spends on reactive tasks – investigating slowdowns, troubleshooting bottlenecks, responding to IT service requests from line-of-business (LOB) stakeholders, and interrupting end user activity to apply patches and upgrades – adds up to lost productivity for the IT organization and the entire enterprise.
The longer an IT organization “drags the anchor” in reactive mode, the slower your business moves, and the harder it is to pick up momentum on the other side of the disruption.
Proactive time builds momentum
AIOps puts your team in a proactive mode, essentially putting infrastructure and apps on autopilot through intelligence and automation. Proactive systems anticipate potential issues and prevent unnecessary slowdowns, feeding forward progress of the business and letting it build momentum.
Because AIOps helps applications and infrastructure fade into the background, successful organizations are readily deploying the emerging technology. MIT recently interviewed customers, analysts, and IT leaders to learn how AI-powered autonomous operations help IT organizations plan and manage infrastructure, handling routine operations automatically to ensure their environments are agile and always-on. The MIT Technology Review Insights paper clearly shows how AIOps helps these IT professionals speed operations and focus their energy on growing the business.
Without the disruption of downtime, end users can rely on applications and infrastructure to support the speed of the business. The right AIOps platform can predict and head off some of the most time-consuming problems that IT admins face, improving the value of time for your business.
Value of offloading IT operations
If your IT platform cannot provide cloud-like agility, the organization can get mired in simply managing hybrid infrastructure, with no time left to extract value from data. But that doesn’t mean all data should reside in the cloud. While it does offer never-before-seen operational efficiencies, public cloud isn’t the destination for every workload.
Data gravity dictates where a workload should run – on-premises, in the cloud, or a combination of the two – and once again, time is a critical factor. As Denis Vilfort at Hewlett Packard Enterprise points out, “the value of data typically declines over time. The newer the data, the more valuable it is. This is especially true for data captured in real time, which needs immediate processing to yield value.” Which means, for maximum efficiency and agility, you must store data in the appropriate location to allow the fastest access to data with the most weight.
Modern AIOps platforms deliver the cloud operational experience wherever you need it: on-prem, in the cloud, or in a hybrid environment. The cloud operational experience brings with it simplicity and automation, helping you assess data gravity and store data appropriately, allowing IT to focus on building next-gen applications for the business without worrying about the underlying infrastructure.
Value of self-service IT
Self-service IT is all about time. For businesses to move quickly, nothing can be more efficient than giving the right controls directly to developers and LOB stakeholders. In a typical IT environment, developers and LOB stakeholders must request capacity and access to data as they embark on each new project. According to a 2021 ESG Master Survey, 52% of organizations say it takes at least 24 hours for end users to get access to requested data. Imagine the agility improvements the enterprise gains if these stakeholders can provision application workloads using a point-and-click experience purely based on SLA requirements, eliminating the back-and-forth with enterprise IT, for instance, on provisioning capacity or performance headroom.
Three imperatives to make the most of your time
To maximize the value of time, businesses need to move from reactive responses to application and infrastructure issues, to proactive actions. The following are key imperatives to becoming more proactive:
- Always-on infrastructure: Infrastructure has become increasingly complex in the recent years. The correlations among the different layers of infrastructure are just too many for humans to keep track of. And when failures occur, it is impossible to manually skim through logs and triage issues in any reasonable timeframe. The solution here is an always-on infrastructure that can predict forthcoming issues and take proactive actions to avoid failures.
- A cloud operational experience: For enterprise IT, experience is the game changer. A cloud operational experience encompasses everything from one-click application provisioning to predicting application performance anomalies and addressing them before downtime strikes. The underlying infrastructure complexities – provisioning, SLA adherence, data management, capacity, and performance headroom management – are abstracted away, and infrastructure management is automated into a “behind-the-scenes” activity.
- Shift to modern IT Ops: No leader wants their team to operate in a reactive manner. To move the business forward, you need recommendations that are predictive and prescriptive in nature, and analytics contextual to your environment. Moreover, with perpetual shortage of IT talent, more leaders are looking to platforms that offer a true self-service experience without requiring domain expertise. These platforms allow IT to focus resources on building use cases critical to the business and transforming operations to being more app-centric.
Make your IT future-proof
In addition to these considerations, look for capabilities that can make your choice of AIOps solution future-proof:
- Cross-stack analytics: Most organizations run their workloads on a combination of containers, virtual machines, and bare metal. Visibility into your middleware software is critical to understanding, for instance, container management and under- or over-provisioning of VMs, enabling you to appropriately align resources to workloads.
- Application-level insights: Visibility shouldn’t stop at the virtualized software layer. In order to transform IT operations to be app-centric, you should have visibility all the way up to your applications. This includes contextual insights, predictive analytics, and anomaly detection for all your apps, along with a topology view of the infrastructure supporting these apps.
- Enterprise support automation: App disruptions can result in sleepless nights for IT, with hours spent on the phone with customer support in attempts to bring the application back up. On their side, vendors often end up troubleshooting and resolving the same issue again and again as it appears in multiple customer environments. AIOps eases the burden for both the vendor and the customer. Once an issue is found and fixed for one customer, AI can predict when that issue would appear in a different customer’s environment. Moreover, based on the learnings from the triage steps that resolved the first customer’s issue, that resolution can be automated and downtime averted for every customer thereafter.
- Relevant datasets: There is no AI without relevant data – lots of relevant data. AIOps can be designed ground-up with data collection at its heart. It is important to note, though, that only performance data should be gathered from the different layers of the infrastructure stack, not customer data. Large volumes of such datasets are crucial for training machine learning models as well as for improving the accuracy of these models over time.
HPE InfoSight offers all of these capabilities, acting as a one-stop shop that provides a simpler way to manage complex environments. Through intelligence and automation, HPE InfoSight transforms IT operations and improves the value of time for organization leaders. Learn more about the industry’s most advanced AI for infrastructure.
To learn more about how AIOps is changing the lives of IT leaders, check out this business paper from MIT Technology Review.
About Ronak Chokshi
HPE
Ronak leads product marketing for HPE InfoSight at Hewlett Packard Enterprise. Prior to HPE, he worked at tech start-ups and large corporations including H2O.ai, MapR, HCL Technologies, TDK InvenSense and Marvell. Through his two decades of B2B product marketing and GTM experience, he has led product launch, worldwide sales enablement, content development, and funnel creation activities across AI, ML, cloud, MLOps, data management and IoT technology domains. Ronak holds a master’s degree in Information Networking from Carnegie Mellon University and a bachelor’s in Electronics Engineering from India. He resides in the San Francisco Bay Area with his family.
