so as we show organizations how they can leverage the power of the servicenow predictive ai ops solution we want to help you and your team understand how they can transform the it experience and gain a competitive edge using business context and machine learning to avert outages and resolve resolve i.t issues proactively so let's take a look at our standard grouping of it business services and you can probably relate to this there is a mix between essential trivial and of course critical applications that drive revenue and customer consumer facing services then there are event sources enrichment data that come from various sources like your cloud infrastructure on-prem servers storage etc these event sources produce data that are then forwarded to our event management solution and this event-driven data can come from a variety of sources anything from familiar monitoring tools like solarwinds or native cloud monitoring tools as well as log aggregators with all of this rich infrastructure data this is where our aiops engine really shines the platform is able to help your team achieve 90 noise reduction because disparate events about ci's are correlated to actionable alerts and root cause is automatically highlighted along with impact analysis this is helpful to know how a business service is being impacted and how any changes may affect other services in your environment using statistical models and the existing event data metric anomaly detection facilitates the ability to predict that a ci is at risk of causing an outage furthermore the advanced correlation function allows incoming events to be correlated to a grouped alert and prevent swivel chair problem resolution that many it teams face with our solution you have actionable information in one workspace view and collectively with our extremely powerful log analytics engine our solution is far more powerful and comprehensive and differentiated from our competitors one because alongside the other elements in the platform we are able to ingest log data from disparate sources to preemptively and proactively draw conclusions of eminent problems within the environment at which point operations teams can take action before an issue occurs in your infrastructure and there is real financial time saving value here so the predictive ai op solution gets you out of a reactive state by predicting problems before occurrence and quickly preventing outages by identifying root cause and achieving significantly lower p1 p2 incidence and ultimately that leads to a 40 reduction in mean time to resolution and as we discussed earlier progressing along that maturity curve towards automation and preemptive problem resolution by the system so that journey towards a self-healing data you