It is important to note the difference between traditional Business Intelligence (BI) and data science. BI is concerned with aggregating and analysing past data and visualising it. Data science is more focused on modelling past data in order to predict future trends. BI is therefore focused on descriptive analytics whereas data science is focused on predictive or prescriptive analytics.
There are also tools in the data science toolbox for advanced descriptive analytics. This comes to light in how a root cause analysis is performed when using BI or data science. The BI analyst will for example have to hypothesize about the root cause, and then plot or aggregate the applicable variables to determine whether they are in fact the correct indicators. For example, when trying to determine why orders are late, the BI analyst would plot indicators against the label of the order being late or on time. The trends may then show which indicators are positively correlated with the order status.
A data scientist would rather categorise orders by their status, compile a model that describes the data, and then look at the feature importance of the model variables to try and determine the leading indicators that show what causes an order to be late.