To contend with the development, IT, and business trends that have shaped enterprises in recent years, teams have to gain radical improvements in their insights and operations. Advanced AI platforms integrate business, development, and operations data to generate actionable insights—enabling teams to continuously improve decision making and the execution of digital initiatives, so they yield enhanced business outcomes. In our prior post, we examined some of the key architectural approaches that characterize an optimal platform. In this post, we examine the key advantages the platform provides.
The Advantages of Advanced AI Platforms
Through employing a range of patented and differentiated capabilities and features, advanced AI platforms provide a wide range of advantages to customers:
Maximizing data utility. With advanced AI platforms, teams can combine data from all products into one model, so algorithms can be used to evaluate everything—maximizing the value of data captured and aggregated.
Facilitating effective analysis. By describing the realities of an environment with a unified data model, the platform enables teams to have robots take on a range of analysis use cases. With these capabilities, advanced AI platforms enable teams to conduct analysis that can be very broad in scope and also very precise.
Powering intelligent automation. The platform’s combination of a unified data model and applied expertise enable teams to establish intelligent automation that offloads a range of tasks from human analysts.
The following sections offer more details on the unique advantages advanced AI platforms provide.
Contextualized, Correlated Intelligence
If operations teams are only looking at infrastructure monitoring, they may see there’s a corrupted RAID drive. At that point, they’ll have to look in other tools and work with other teams to assess the impact, determine whether it is important, and so on. This effort can demand a lot of time, effort, and expertise.
Advanced AI platforms streamlines these efforts by providing the power of multi-layer visibility and correlation. With the platform, teams can unify intelligence from different domains. The platform can incorporate different ontologies to create a uniform playing field for data that algorithms can run on. As different data sources are incorporated, they are integrated into the property graph, building intelligence based on what is known about an entity.
The platform provides cross-domain context transfer that enables silos to be broken down. Teams can leverage what they know from one product and what they just learned from another product to react more swiftly and intelligently. For example, if a problem arises in hardware, operators can now evaluate its impact within the context of the application or end user experience. They can therefore see immediately that the hardware issue is the cause of a delay in a critical service, such as shopping cart transactions, and escalate the matter accordingly.
Current, Complete, and Accurate Model of the Run-time Environment
Among disparate teams, establishing and maintaining a current picture of an IT environment has been a significant challenge. An architect can draw up an intended deployment diagram. As soon as operations deploys it, the details of that architecture can start to change. For any number of reasons, the components may not be deployed exactly as designed. Systems may immediately need to be scaled up or down. Particularly as environments continue to grow more complex and dynamic, gaining a clear, accurate picture of the operating environment only gets more difficult and time consuming.
One of the significant advantages of advanced AI platforms is that it enables teams to establish—and maintain—a current, complete, and accurate picture of the run-time environment. Instead of being constrained by visibility into a single architectural layer or technology, teams can view a model that’s based on the multi-dimensional reality of the environment. Teams don’t have to rely on old diagrams, or keep manually creating models that quickly become out of date.
Teams can leverage visibility that affords a much higher degree of accuracy. In addition, through advanced AI platforms, robots can be employed to apply machine learning approaches to manage clustering, correlating, and analyzing these comprehensive data sets.
Teams can work from a unified data model that provides comprehensive descriptions of environments and how they operate. Rather than just a static, point-in-time snapshot, advanced AI platforms track the history of all events and relations. This enables teams to see everything happening in the data center, and to track changes and trends within the environment through a user interface and algorithmic analysts.
This multi-layered visibility means an analyst can immediately identify that a power supply failed, understand which software is running on the affected machine, and see how application performance, end users, and even business metrics are being affected.
Advanced AI platforms also enable teams to get invaluable insights into how the environment evolves. For example, an organization may be running applications with 20 distributed components, all interacting with each other. With advanced AI platforms, teams can immediately see that, after one of the components was upgraded, a bug was introduced. Further, they can then see how that bug affected other components.
With advanced AI platforms, business, IT, and development teams can be better equipped meet their ever-intensifying demands. Advanced AI platforms enable teams to gain a unified view of their dynamic business and IT landscape, and leverage the automation and insights needed to realize optimized decision making, operational execution, and business outcomes.
Formerly the chief architect of DX APM and now the chief architect of Broadcom AIOps, Erhan Giral has been working in monitoring space for over twelve years and holds various patents around operational intelligence and application monitoring. Before joining Broadcom, he worked on various graph visualization and analytics frameworks and SDKs; servicing diverse verticals such as network intelligence, bioinformatics and finance.