Data Analytics Lifecycle

Key Concepts


Data preparation

Model planning

Model execution

Communicate results


Data science projects differ from most traditional Business Intelligence projects and many data analysis projects in that data science projects are more exploratory in nature. For this reason, it is critical to have a process to govern them and ensure that the participants are thorough and rigorous in their approach, yet not so rigid that the process impedes exploration.

Many problems that appear huge and daunting at first can be broken down into smaller pieces or actionable phases that can be more easily addressed. Having a good process ensures a comprehensive and repeatable method for conducting analysis. In addition, it helps focus time and energy early in the process to get a clear grasp of the business problem to be solved.

A common mistake made in data science projects is rushing into data collection and analysis, which precludes spending sufficient time to plan and scope the amount of work involved, understanding requirements, or even framing the business problem properly. Consequently, participants may discover mid-stream that the project sponsors are actually trying to achieve an objective that may not match the available data, or they are attempting to address an interest that differs from what has been explicitly communicated. When this happens, the project may need to revert to the initial phases of the process for a proper discovery ...

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