A new Performance Analytics and Decision Support (PADS) framework linking advanced performance management and big data analytics technologies will emerge in 2014. The PADS framework enables organizations 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. A more holistic approach that breaks down data silos across different IT teams and departments is the path to assuring service delivery, gaining deeper systems and customer insights, and improving operational efficiency.
The Big Data Challenge
As IT groups acquired discrete tools that focused on a particular hardware, network or software issue, many organizations have ended up with a patchwork quilt of point solutions that do not work well together. And while each tool might be indicating that performance of a particular segment or component is “normal”, outages persist and the actual user experience continues to disappoint.
New distributed computing architectures and 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. As a result, more components of the application delivery chain are obscured from IT and line of business owners.
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. Traditional performance monitoring solutions for application, network, infrastructure and business transactions have become overwhelmed by the scale of data required to comprehensively manage application performance.
The proliferation of server virtualization and the tools needed to monitor and manage virtualized dynamic infrastructures and highly distributed application architectures only expand the data points and metrics that need to be analyzed.
Consequently, vital information is often overlooked, resulting in missed opportunities to uncover hidden patterns, relationships and dependencies. Additionally, whatever data is gathered is not normalized or time synchronized, making analysis and rapid problem resolution impossible. Yet pouring more data into obsolete analytics tools only compounds the problem.
Making Performance a Priority
Performance visibility and greater operational intelligence should be paramount to all organizations amid rising systems complexity and unabated data growth. 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.
The common components of ROI – reduced operating costs, enhanced business productivity, and incremental revenue generation – are closely associated with application performance. Service outages can be quite costly. Depending on the industry sector, slow responsiveness or complete outage (brownouts or downtime) of a company's most business critical application can cost between $100,000 and $1 million per hour. The fallout from poor transaction performance can be a loss of customers, regulatory fines and damage to firm reputation.
Nothing shines a light on an IT team's success or failure as application performance and availability. With uptime as their priority, they need to adapt a more holistic approach to performance management and decision analytics. Through best practices, they can help their companies leverage IT investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness.
A New Generation of Performance Analytics Techniques
More enterprises have recognized the need for a new generation of performance analytics techniques that go beyond the scope of traditional monitoring tools, which were designed for smaller and more static environments.
These new performance analytics techniques must help the enterprise in three ways:
First, enterprises need to understand what levels of performance (i.e. speed and availability) are needed from their increasingly cloud-based and mobile applications in order to deliver fast, reliable and highly satisfying end-user experiences. To better understand the properties of the 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. A comprehensive performance analytics platform provides visibility across the entire application delivery chain – from behind the firewall and out to the Web, including third-party cloud providers.
Second, 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. Enterprises need to measure the true experiences of their most important end-user segments, including those that are remote and mobile.
Third, to provide insights that line of business users can understand and value, IT must establish an effective link between performance management and analytics.
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.
Find out more about PADS: Introducing the Performance Analytics and Decision Support (PADS) Framework- Part Two
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