Database Reference
In-Depth Information
guidance on developing decision models, calibrating expert estimates, and
deriving the expected value of information.
• “
MAD Skills
” by Cohen et al. [7] offers input for several of the techniques
mentioned in Phases 2-4 that focus on model planning, execution, and
key findings.
Figure 2.2
presents an overview of the Data Analytics Lifecycle that includes six
phases. Teams commonly learn new things in a phase that cause them to go back
and refine the work done in prior phases based on new insights and information
that have been uncovered. For this reason,
Figure 2.2
is shown as a cycle. The
circular arrows convey iterative movement between phases until the team
members have sufficient information to move to the next phase. The callouts
include sample questions to ask to help guide whether each of the team members
has enough information and has made enough progress to move to the next phase
of the process. Note that these phases do not represent formal stage gates; rather,
they serve as criteria to help test whether it makes sense to stay in the current
phase or move to the next.
Figure 2.2
Overview of Data Analytics Lifecycle
Here is a brief overview of the main phases of the Data Analytics Lifecycle:
