Chapter 12 Report

The last two steps in the process of doing a data science project are “Results Interpretation” and “Report and Communication”. This chapter will explain the tasks and how to accomplish the tasks in these two final steps.

Results Interpretation. There are three different explanations about the Results interpretation.

  1. The first is explanation is from business people. They require data scientists to interpret or explain any results and findings from the analyses. These results or findings generally regard any patterns or insights discovered by the data analyses. The purpose is to provide reasons for and build credibility about the results. providing explanations can build a kind of trust in the results. This may require reporting on the process and methods used in the process to produce the results.

  2. The second understanding of the Result interpretation is the kind of data scientist’s own assessment or evaluation of any findings. This is to find out if there are any defects or places for improvement. It was demonstrated in the process of Data modelling in the previous chapters.

  3. The third explanation about the Results interpretation particularly in the area of business intelligence (BI). Results interpretation means an assessment of the business impact of the analytical results. This can only be done by the business people who have the domain expertise.

What we mean by the results interpretation here is a summary of all the efforts we have put into the data science project so far. Recognising data analysis may be an iteration process that may need continuous efforts, we should do a periodical summary and layout any further analyses necessary.

Report and Communication means to produce a narrative of the results, and draw a conclusion on how close the results address the original problems, and communicating about the outputs of analyses with interesting parties. When communicating with non-technical people, visual forms are more intuitive and worth a lot of words. So It makes sense to use data visualization tools or dashboards to communicate with people for your analytical results.