The diagnostic analysis is also retrospective, but instead, it looks for the reason
what happened to the problem that was presented in the descriptive analysis.
Diagnostic analysis is the essential next step after a
company perform a descriptive analysis.
Diagnostic analysis also takes advantage of historical data from a
company from various internal sources.
This analysis is more complex and requires data analysts to use
multidimensional data structures, statistical algorithms and
slice-and-dice, drill-through, drill-down, roll-up, etc. capabilities for
to be able to quickly and easily find the causes of problems, patterns,
trends and correlations.
The biggest advantage of diagnostic analytics is being able to provide
context to a business problem through a series of data models.
Diagnostic analysis can help resolve some issues that are not
can be answered simply by viewing dashboards and reports.
For example, what caused a sudden drop in search traffic for a
website for no obvious reason? A diagnostic tool can
tell you that you have an unbalanced link distribution between pages
internal and that caused the fall.
