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.
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