Database Reference
In-Depth Information
overview of the data and perform further exploration on specific areas. It
also points the team to possible areas of interest within the data.
•
Review the raw data:
Obtain preliminary data from initial data feeds.
Begin understanding the interdependencies among the data attributes,
and become familiar with the content of the data, its quality, and its
limitations.
•
Evaluate the data structures and tools needed:
The data type and
structure dictate which tools the team can use to analyze the data. This
evaluation gets the team thinking about which technologies may be good
candidates for the project and how to start getting access to these tools.
•
Scope the sort of data infrastructure needed for this type of
problem:
In addition to the tools needed, the data influences the kind of
infrastructure that's required, such as disk storage and network capacity.
Unlike many traditional stage-gate processes, in which the team can advance only
when specific criteria are met, the Data Analytics Lifecycle is intended to
accommodate more ambiguity. This more closely reflects how data science projects
work in real-life situations. For each phase of the process, it is recommended to
pass certain checkpoints as a way of gauging whether the team is ready to move to
the next phase of the Data Analytics Lifecycle.
The team can move to the next phase when it has enough information to draft
an analytics plan and share it for peer review. Although a peer review of the plan
may not actually be required by the project, creating the plan is a good test of the
team's grasp of the business problem and the team's approach to addressing it.
Creating the analytic plan also requires a clear understanding of the domain area,
the problem to be solved, and scoping of the data sources to be used. Developing
success criteria early in the project clarifies the problem definition and helps the
team when it comes time to make choices about the analytical methods being used
in later phases.
