Introducing the Performance Analytics and Decision Support (PADS) Framework - Part Two
April 10, 2014

Gabriel Lowy
TechTonics

Share this

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.

Start with Part One of Introducing the Performance Analytics and Decision Support (PADS) Framework

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.

Gabriel Lowy is the founder of TechTonics Advisors, a research-first investor relations consultancy that helps technology companies maximize value for all stakeholders by bridging vision, strategy, product portfolio and markets with analysts and investors
Share this

The Latest

November 21, 2024

Broad proliferation of cloud infrastructure combined with continued support for remote workers is driving increased complexity and visibility challenges for network operations teams, according to new research conducted by Dimensional Research and sponsored by Broadcom ...

November 20, 2024

New research from ServiceNow and ThoughtLab reveals that less than 30% of banks feel their transformation efforts are meeting evolving customer digital needs. Additionally, 52% say they must revamp their strategy to counter competition from outside the sector. Adapting to these challenges isn't just about staying competitive — it's about staying in business ...

November 19, 2024

Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey ...

November 18, 2024

SLOs have long been a staple for DevOps teams to monitor the health of their applications and infrastructure ... Now, as digital trends have shifted, more and more teams are looking to adapt this model for the mobile environment. This, however, is not without its challenges ...

November 14, 2024

Modernizing IT infrastructure has become essential for organizations striving to remain competitive. This modernization extends beyond merely upgrading hardware or software; it involves strategically leveraging new technologies like AI and cloud computing to enhance operational efficiency, increase data accessibility, and improve the end-user experience ...

November 13, 2024

AI sure grew fast in popularity, but are AI apps any good? ... If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment ...

November 12, 2024

If you were lucky, you found out about the massive CrowdStrike/Microsoft outage last July by reading about it over coffee. Those less fortunate were awoken hours earlier by frantic calls from work ... Whether you were directly affected or not, there's an important lesson: all organizations should be conducting in-depth reviews of testing and change management ...

November 08, 2024

In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...

November 07, 2024

On average, only 48% of digital initiatives enterprise-wide meet or exceed their business outcome targets according to Gartner's annual global survey of CIOs and technology executives ...

November 06, 2024

Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...