figure - the crisp-dm model depicting the data mining lifecycle figure - clearly shows there are total of six major phases in the data mining lifecycle and the direction to proceed is depicted with arrows this model is not rigid imposition but rather framework to ensure you are on the right track when going through the lifecycle of any analytics project in some scenarios like anomaly detection or trend analysisyou might be more interested in data understandingexplorationand visualization rather than intensive modeling each of the six phases is described in detail as follows business understanding this is the initial phase before kick starting any project in full flow however this is one of the most important phases in the lifecyclethe main objective here starts with understanding the business context and requirements for the problem to be solved at hand definition of business requirements is crucial to convert the business problem into data mining or analytics problem and to set expectations and success criteria for both the customer as well as the solution task force the final deliverable from this phase would be detailed plan with the major milestones of the project and expected timelines along with success criteriaassumptionsconstraintscaveatsand challenges