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Driving Business Decisions Off Data By @AppDynamics | @DevOpsSummit [#DevOps]

There are multiple systems in use for basing business decisions

By Jonah Kowall

There are multiple systems in use for basing business decisions. The popular business intelligence (BI) market focuses on the use of back office data that must be aggregated or otherwise centralized and sliced and diced to make business decisions. While this is clearly critical data, and BI is a $14.4B market according to Gartner (calendar year 2013). Software-defined businesses need a far more real-time view of the front office and systems of engagement. These systems of engagement change far more quickly and require real-time response, similar to running IT Operations versus many other parts of IT. These analytics technologies will not only be used to guide product decisions, but also to enable a fluid organization.

Gartner predicts by 2017, 70 percent of successful digital business models will rely on deliberately unstable processes designed to shift as customer needs morph. (See: http://www.gartner.com/newsroom/id/2866617 )

As a result of a fluid business model, and fluid decision-making to drive innovation, the processes must be adjusted and adapt to this change. The adaptation creates instability in processes, but it’s essential to meet customer preferences and demands. The agile product, agile development, and agile organizations require adaptation and experimentation based on customer interaction. The business transaction marries together the user with the processes customers interact with, making that the discrete focus of monitoring. This monitoring must be captured, typically by APM tools such as ours, or by writing custom instrumentation within the software, and finally sent into analytics engines that can provide the insight.

We still have a significant problem in the industry, which is evident in the monitoring space. We have a dashboard problem, and buyers make decisions on dashboards, not insight. The love of dashboards continues to proliferate tool fragmentation and dashboards.

Today’s analytics are user driven, where the user of the tool or product is driving the analysis and making decisions. With advances in machine learning (See: http://www.quora.com/What-are-the-important-advances-in-machine-learning-over-the-past-decade) we are starting to see these new algorithms being applied to IT Operations data and ITOA which will allow for machine driven analytics and insight. This significant shift will have repercussions across IT Operations Management.

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