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A new Performance Analytics and Decision Support Framework, linking advanced performance management and big data analytics technologies, will emerge in 2014.  The PADS framework enables enterprises and service providers to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex IT systems across the entire application delivery chain.

PADS establishes best practices for assuring user experience, reducing risk and improving operational decision making in a more efficient, secure and timely fashion.  To get the full report click

Distributed computing architectures and new approaches to agile application development have made computing far more scalable and dynamic than ever before.  But cloud, mobile and social megatrends have also resulted in unprecedented levels of complexity.  While these shared services and compute resources are managed centrally, they may be controlled by either the enterprise or by external providers.  As a result, more components of the application delivery chain are obscured from IT and line of business owners.

Meanwhile, data creation and growth continues unabated from an ever-increasing number of sources – both internally and from outside of the organization.  Yet despite the wealth of data and content available today, most business users continue to struggle to access information they need to gain deeper insights into the business for better and faster decision making.

Big data is overwhelming legacy systems and obsoleting traditional monitoring tools.  Vital information is often overlooked, resulting in missed opportunities to uncover hidden patterns, relationships and dependencies.

Numerous surveys have shown high availability of applications as the top priority of business users, customers and CIOs.  But the more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation.  Slow responsiveness or complete outage of a firm’s most business critical application can cost between $100,000 and $1 million per hour.  Poor transaction performance can result in lost customers, regulatory fines and reputation damage.

The need for a PADS framework emerges

Uptime is the benchmark of an IT team’s success or failure.  Cloud-based architectures and increased mobility, coupled with the traffic explosion of latency-sensitive applications necessitate a more holistic approach to performance management and decision analytics.

Traditional performance monitoring solutions have become overwhelmed by the scale of data required to comprehensively manage application performance.  Whatever data is gathered is not normalized or time synchronized, making analysis and rapid problem resolution impossible.  To better understand the properties of system components and their place in the overall application delivery chain requires a higher-level assessment of the relationships to each other as well as to the wider system and environment.

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.

Leveraging gains in processing power and storage capacity, IT organizations can extract and analyze more performance-related data points across the entire application delivery chain to gain deeper intelligence.  This includes external facilities that are not controlled by the organization.

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; and,
  2. enable IT to become a strategic provider and orchestrator of internally and externally sourced services to business units that can leverage operational intelligence.

The “point of delivery”, which is where the user accesses a composite application, is the only perspective from which user experience should be addressed.  As such, the most relevant metric for any IT organization is not about infrastructure utilization.  Instead, it is at what point of utilization the user experience begins to degrade.

IT can then feed this information about the application delivery chain and user experience upstream into an operational intelligence (OI) platform.  An OI platform collects, indexes, correlates and analyzes log and other forms of machine data at massive scale to help IT organizations and line of business users gain real-time insights from disparate data types and sources.  These data sources can range from sensors and social media feeds to NoSQL data platforms such as Hadoop.

Consolidating this data to make it readily searchable can reveal previously undetected patterns or unique events.  The value of this information allows users to quickly identify and troubleshoot systems, investigate security incidents and demonstrate compliance efficiently and cost effectively.  When combined with historical data in traditional BI systems and data warehouses and newer data discovery tools that provide easy-to-use visualization techniques, the broader intelligence users gain drive better operational decision making.


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.  In addition to providing pre-emptive warnings of systems failure, the framework assures application availability and user experience as well as flexible scaling. 

While no one solution can provide everything, deploying too many solutions only increases complexity, cost and frustration.  The highest performing enterprises use no more than three solutions, with forward-thinking IT teams linking performance management and operational intelligence platforms.  To get the full report click

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Tags: Analytics, Big, Data, Decision, Framework, Intelligence, Operational, PADS, Performance, Support


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