Over the last year there have been an influx of new graphical user interfaces (UIs) as monitoring and dashboard vendors scramble to keep up with large enterprises increasingly focused on understanding the business impact of IT, and Netuitive is no exception.

Today’s enterprises are challenged with monitoring and interpreting increasing volumes of infrastructure performance, application data, and business metrics.  Like Big Data, advanced predictive analytics have moved to the forefront for their ability to identify anomalies that could lead to business and revenue impacting performance problems.  As such, the UIs visualization capabilities have become an increasingly important means to this end, and ultimately to proactive management of critical application performance.

But to understand where the UI is now, and where it must go, one must look back at how APM use cases have evolved and changed.

In the “old” world, the Big Four and APM tool vendors developed dashboards that monitored individual data sources visualizing results in green, yellow, and red buttons, bar charts, and pie graphs.  Basically, all these did was show when a server or storage platform was either over-utilized or broken, and that you needed to physically add capacity and/or replace it.  They were no sophisticated algorithms and it was up to the user to interpret any impact on IT performance now and in the future.

Over time, data sources continued to increase in volume and variety, virtualization happened, and complexity increased to the point of making it  humanly impossible to monitor and interpret IT and application performance … let alone understand business impact.

To address this, IT operations, particularly those large enterprises with mission critical applications, began using IT analytics to monitor real-time data and metrics to identify anomalies that were leading indicators of future performance problems.

Fast forward to 2013.  The volume of performance and application data and metrics are orders of magnitude higher and IT operations analytics are now central to any advanced monitoring strategy. As such, the demands on the monitoring tools have changed significantly.  It is no longer simply about green, yellow and red buttons.  While they are still primary indicators of the “health” of an application or business service, it is now about being able to process and automate the analysis and correlation of multiple data types with analytics that detect and interpret anomalies.  Today’s tools can store and process large quantities of information.  Advanced algorithms assist in highly synthesized, meaningful visualization that you can use it for decision support.

However, the industry overall is still struggling to develop processing that is sophisticated enough to identify the “anomaly in a haystack” that may be the key indicator of future performance degradation and/or service interruption.

The focus is now on developing UIs that assist in the detection of anomalies and allow you to act on them in real time.  And not every anomaly is a problem.  You need to be able to filter them.  And when an anomaly does happen, you need to quickly determine 1) whether or not this anomaly is going to lead to a real problem? 2) and if it is an early indicator, what do you do about it? The new UIs are focused on helping you address that.

In Netuitive’s case, we focus on the IT infrastructure, application data, and key business metrics associated with a particular mission critical application or business service with the goal of understanding business impact.  We integrate our predictive analytics technologies as part of large scale APM monitoring solutions that focus on the key IT performance, application data, and business metrics for a particular business service instead of trying to boil the ocean across the enterprise. We bring all of this analysis and correlation for a particular business service or application together in our new UI that visualizes the correlation of IT performance, application data and key business metrics in a single screen at your fingertips.  It also integrates with local knowledge bases to further extend its ability to distinguish between true anomalies and known trends based on historical behaviors.   It is essentially a bridge from the old approach to the new.

Looking forward the UI will need to continue to evolve. Data will continue to increase, analytics will need better algorithms, and the goal will continue to be about finding anomalies and addressing them before an issue cascaded into a larger performance problem or service interruption.  The UI will need to process large amounts of data and help you determine if an anomaly is a problem or just an anomaly, and what do you do about it…proactively.

Ultimately it is this proactive approach that allows you to prevent impact on the business.  And to achieve this, the new UIs need to help you understand the impact on the business.

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