Not invasive approach which allow to leverage already existing information; clients don’t have to start the process from scratch – yet again. TeamQuest doesn’t need any data transfer (ETL Extract, Transform, Load), but it automatically connects to the DB in use. This architecture (unique in the market) guarantees a quick implementation and immediate results.
- Connection modules to your main Monitoring solutions. Connections to other tools, developed from time to time, are available out of the box and can be easily integrated. Should a connection to a DB not be available, it can be developed in a very short time.
- Report creation doesn’t require any programming language knowledge. By using the drag-and-drop interface, a user can easily apply to the available data the most common analytical functions such as: Correlation, Range/Date Categorization, Trending, Condition/Date/Time Filtering, Sigma, Standard Deviation, Intercept, R-Squared Pareto, Percentile, etc.
Performances (SPEC CPU2006 Int Rate tests) of the Hardware currently available in the market are stored – and constantly updated – in TeamQuest Model (this list is updated monthly). By combining this information with the system utilisation data (from Tivoli or other Monitoring tools) and with Business data, TeamQuest Model presents a highly accurate prediction of future What-If scenarios based on the user inputs and on a highly sophisticate proprietary mathematical model. This allows finding the optimal configuration of the IT Systems taking into consideration the balance between the organisation’s actual needs and cost constraints. TeamQuest Model is used in consolidation projects (Physical and Virtual), platform migration, response time prediction of components in case of requests’ increment, and in other custom cases.
- TeamQuest predictive capabilities are based on queuing theory: it calculates the current and future Services’ performances (through a parameter called TPI, TeamQuest Performace Indicator) rather than just monitoring the symptoms. This automatic process analyses and predicts the non-linear systems’ behaviour, reducing the risk not to have enough resources to support the agreed SLAs.