Today’s digitally savvy customers are tough and demanding. They expect engaging experiences and near real time answers and solutions to their questions and problems. This puts enormous pressure on businesses to provide excellent performance and contextual solutions. This is where predictive analytics comes in. It enables businesses to harness data and identify opportunities and risks that lie ahead to improve performance and mitigate risks.
For instance, on the operations side, system administrators have traditionally struggled with reactive modes of operation, and delays in detecting system issues and outages.
While many enterprise monitoring systems have threshold based alerting capabilities, it’s often a case of too little too late, given the turnaround time involved for human intervention.
Being web-scale today has a new meaning. Next generation platforms support a myriad micro services deployed across 1000s of servers and multiple geographies, emitting millions of system metrics every day. The true value of these metrics can be realized only by deploying sophisticated predictive models and anomaly detection techniques that help determine hidden patterns and predict outcomes with probabilistic likelihoods. These outcomes, when combined with self-healing techniques (like auto scaling), elevates operational analytics and intelligence to the next level.
The key differentiator that enables organizations to tap the complete potential of prediction algorithms is its coupling with prescriptive theories. In essence, ‘Don’t just tell me what will happen, tell me what I should do when it happens?’ is the theoretical basis for prescriptive analytics. For example, consider a sample indicator (like traffic throughput) in a large cluster. This metric will show an oscillating pattern of rise and fall, peaking in evenings and troughing around mid-night. When this pattern is processed through a self-learning analytics platform, it will not only be able to accurately predict traffic increase for an upcoming holiday, but also spawn additional servers to tackle this traffic in a true self-healing ecosystem.
Predictive analytics applications
GAVS uses predictive analytics extensively in the space of IT service operations. The self-learning analytics platform gathers data on past performance indicators on servers, key applications, network and storage. Petabytes of log data is crunched to arrive at meaningful inflection points that govern the alert system. For example, if the server load on a typical Tuesday afternoon is 50%, and on one instance let’s say the load increases to 75%, it could trigger an alert. However, the 75% may also be an exceptional instance due to a specific activity spike in the application and may need no action. Here, real-time monitoring of application usage and user logins will provide a systemic insight into the coupling of applications and server. Based on historical data of the relationship between the two entities and the current state of these variables, future actions are determined.
GAVel is a Big Data platform from GAVS that uses predictive analytics to achieve benefits such as preventing IT outages or reducing high-risk incidents, enhancing the productivity of the command center by reducing turn-around times (TAT), and offering better visibility into TAT. It also offers insights into dependent variables such as the performance of applications due to the server load or network traffic or vice-versa. All these in aggregation, eventually lead to better utilization and management of assets.
Driving operational excellence with predictive analytics
Profitably streamlining operations to meet the ever-changing customer demands and expectations is a business imperative today. Operational decisions are critical to enhanced efficiency and superior customer perception. Enabling resilient process and operational decisions based on predictive analytics can help organizations take customer service to the next level and respond quickly to unexpected market changes – a definitive source of competitive advantage.
