The PADS Framework, for Performance Analytics and Decision Support, represents a more holistic approach to adaptive, proactive and predictive operational data management and analysis. The framework links advanced performance management and big data analytics technologies to enable organizations to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex virtualized and mobile systems across the entire application delivery chain.
The PADS framework connects unified next-generation performance management and operational intelligence technologies into holistic, integrated platforms that consolidate multiple previously discrete functions. These platforms work in concert, as performance data analytics provides physical and logical knowledge of the computing environment to allow for more powerful and granular data queries, discovery and manipulation.
Expect these platforms to evolve further toward operational intelligence by expanding the types of data sources they can collect and correlate. They will also drive deeper into analytics, including predictive capabilities, to allow IT – and eventually, line of business users – to monitor the performance of services more granularly.
The performance analytics platform incorporates network, infrastructure, application and business transaction monitoring (NPM/IPM/APM/BTM), which feeds an advanced correlation and analytics engine. A single unified view of all components that support a service facilitates the management of service delivery and problem resolution.
Within a PADS framework, users can then feed this information about the application delivery chain and user experience upstream into an operational intelligence (OI) platform. The OI platform can then integrate this data with other types of information to improve decision making throughout the organization.
An OI platform not only ingests data from performance analytics platforms, but a far wider variety of machine and streaming data that are in semi-structured or unstructured formats. Consolidating this data to make it readily searchable can reveal previously undetected patterns or unique events. OI platforms provide a more unified view of events, which are often delivered from multiple streams as messages, to enable more efficient correlation and analysis.
The twin missions of the framework are to:
1. Allow IT to be more proactive in anticipating, identifying and resolving performance problems by focusing on user/customer experience.
2. Enable IT to become a strategic provider and orchestrator of internally and externally sourced services to business units that can leverage operational intelligence.
Ultimately, the PADS Framework can help organizations achieve the three return on investment (ROI) objectives:
1. Reducing costs
2. Enhancing productivity
3. Generating incremental revenues
PADS can also be used to secure valuable systems and data, thereby reducing operational risk while ensuring compliance with GRC (governance, regulatory, compliance) mandates.
Analytics: Going Beyond Montitoring
The PADS framework goes beyond real-time monitoring to offer predictive analytics, which is one of the most important market trends. Another is the ability to scale to big data requirements and interface with newer NoSQL databases. In addition to providing pre-emptive warnings of systems failure, the framework assures application availability and user experience as well as flexible scaling.
The performance analytics platform includes real-time analysis of application and service performance across both physical and virtual environments by dynamically tracking, capturing and analyzing complex service delivery transactions across multi-domain IP networks.
Deep-dive analytics allow IT organizations to be more proactive by pinpointing the root cause of problems before users call the help desk and before a visitor departs a website. Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with critical business processes. Capabilities should include data visualization to facilitate mapping resource and application dependencies and allow modeling of applications to detect patterns and predict points of failure.
Data mining that entails analysis of data to identify trends, patterns or relationships among the operational data can be used to build predictive models. Today, modeling is being facilitated by tools that automate iterative, labor-intensive processes. Newer technologies require little or no programming and can be implemented quickly with cloud-based solutions. Predictive models can now be developed by line of business users to improve a business function or process.
The key to success for the PADS framework is providing correlation and analytics engines that feed into customizable dashboards. The ability to quickly visualize and interpret a problem or opportunity that results in actionable decisions is how to derive the most value from the platforms that underlie the framework.
The requirements of an APM tool are now much more complex than they've ever been. Not only do they need to trace a user transaction across numerous microservices on the same system, but they also need to happen pretty fast ...
Performance monitoring is an old problem. As technology has advanced, we've had to evolve how we monitor applications. Initially, performance monitoring largely involved sending ICMP messages to start troubleshooting a down or slow application. Applications have gotten much more complex, so this is no longer enough. Now we need to know not just whether an application is broken, but why it broke. So APM has had to evolve over the years for us to get there. But how did this evolution take place, and what happens next? Let's find out ...
There are some IT organizations that are using DevOps methodology but are wary of getting bogged down in ITSM procedures. But without at least some ITSM controls in place, organizations lose their focus on systematic customer engagement, making it harder for them to scale ...
If you have deployed a Java application in production, you've probably encountered a situation where the application suddenly starts to take up a large amount of CPU. When this happens, application response becomes sluggish and users begin to complain about slow response. Often the solution to this problem is to restart the application and, lo and behold, the problem goes away — only to reappear a few days later. A key question then is: how to troubleshoot high CPU usage of a Java application? ...
Operations are no longer tethered tightly to a main office, as the headquarters-centric model has been retired in favor of a more decentralized enterprise structure. Rather than focus the business around a single location, enterprises are now comprised of a web of remote offices and individuals, where network connectivity has broken down the geographic barriers that in the past limited the availability of talent and resources. Key to the success of the decentralized enterprise model is a new generation of collaboration and communication tools ...
To better understand the AI maturity of businesses, Dotscience conducted a survey of 500 industry professionals. Research findings indicate that although enterprises are dedicating significant time and resources towards their AI deployments, many data science and ML teams don't have the adequate tools needed to properly collaborate on, build and deploy AI models efficiently ...
Digital transformation, migration to the enterprise cloud and increasing customer demands are creating a surge in IT complexity and the associated costs of managing it. Technical leaders around the world are concerned about the effect this has on IT performance and ultimately, their business according to a new report from Dynatrace, based on an independent global survey of 800 CIOs, Top Challenges for CIOs in a Software-Driven, Hybrid, Multi-Cloud World ...
APM tools are your window into your application's performance — its capacity and levels of service. However, traditional APM tools are now struggling due to the mismatch between their specifications and expectations. Modern application architectures are multi-faceted; they contain hybrid components across a variety of on-premise and cloud applications. Modern enterprises often generate data in silos with each outflow having its own data structure. This data comes from several tools over different periods of time. Such diversity in sources, structure, and formats present unique challenges for traditional enterprise tools ...
Today's organizations clearly understand the value of digital transformation and its ability to spark innovation. It's surprising that fewer than half of organizations have undertaken a digital transformation project. Workfront has identified five of the top challenges that IT teams face in digital transformation — and how to overcome them ...