A good question. This was asked by one of our customers. She sent me the screenshots as an example:

This was our answer:


Its a great question and the answer has many layers.
We maintain two datasets monthly and annual. The annual data provides a long history and the monthly data offers seasonality analysis. When a monthly number is released by a customs authority it can be revised. This is normally the following month or it can be any point in the future although mor rare. When we perform our standard monthly updates we always re-import the last 3 months, to collect the majority of updates. However, periodically they can make adjustments in previous years.
When annual data is updated annually sometimes these numbers can become out of step with monthly updates.  As time rolls on these series synchronise again but the differences should be very minor.
In the example there were rather larger discrepancies. So what’s going on? The data selected was from “Tanker Departures”. This means we are looking at global country export data. However, you have selected you want to see “imports”. Therefore, in order for us to build the data in your selection, we gather the data from every country in the world, not the country’s data shown . Not every country has monthly data, therefore we have to estimate. The annual data and monthly data have different estimation algorithms and therefore this can lead to larger mismatches when comparing the two series.
Therefore, If I were comparing a country’s imports over time I’d use the “Tanker Arrivals” dataset and vise versa, “Tanker Departures” for exports.
However, in contradiction to this, if I was interested in the very latest import data, I’d use “Tanker Departure”. Because imports are always lagging due to the fact they have been on a  1-6 week journey. Exports give you the very latest on what has just been shipped… so you need a bit of skill in choosing your dataset.