Database Reference
In-Depth Information
to accept or reject a hypothesis. Other times, teams perform very robust analysis
and are searching for ways to show results, even when results may not be there.
It is important to strike a balance between these two extremes when it comes to
analyzing data and being pragmatic in terms of showing real-world results.
When conducting this assessment, determine if the results are statistically
significant and valid. If they are, identify the aspects of the results that stand out
and may provide salient findings when it comes time to communicate them. If
the results are not valid, think about adjustments that can be made to refine and
iterate on the model to make it valid. During this step, assess the results and
identify which data points may have been surprising and which were in line with
the hypotheses that were developed in Phase 1. Comparing the actual results to the
ideas formulated early on produces additional ideas and insights that would have
been missed if the team had not taken time to formulate initial hypotheses early in
the process.
By this time, the team should have determined which model or models address the
analytical challenge in the most appropriate way. In addition, the team should have
ideas of some of the findings as a result of the project. The best practice in this
phase is to record all the findings and then select the three most significant ones
that can be shared with the stakeholders. In addition, the team needs to reflect
on the implications of these findings and measure the business value. Depending
on what emerged as a result of the model, the team may need to spend time
quantifying the business impact of the results to help prepare for the presentation
and demonstrate the value of the findings. Doug Hubbard's work [6] offers insights
on how to assess intangibles in business and quantify the value of seemingly
unmeasurable things.
Now that the team has run the model, completed a thorough discovery phase,
and learned a great deal about the datasets, reflect on the project and consider
what obstacles were in the project and what can be improved in the future. Make
recommendations for future work or improvements to existing processes, and
consider what each of the team members and stakeholders needs to fulfill her
responsibilities. For instance, sponsors must champion the project. Stakeholders
must understand how the model affects their processes. (For example, if the team
has created a model to predict customer churn, the Marketing team must
understand how to use the churn model predictions in planning their
interventions.) Production engineers need to operationalize the work that has been
done. In addition, this is the phase to underscore the business benefits of the
