7.9. Integration of different data sources. To achieve full coverage of the international merchandise trade statistics, data compilers often have to merge and cross-check data collected from customs and non-customs sources, which is a highly complex and time-consuming activity. Merging customs and non-customs data includes adding non-customs data to the customs data and substituting non-customs data for the customs data. For the purpose of quality control and/or for the information of the users, compilers might wish to differentiate data based on customs data sources and data based on non-customs data sources.
7.10. Issues encountered when merging data from different sources. Compilers should need to be aware that the following issues need to be addressed when merging data from different sources:
(a) Different sources may provide different data elements or levels of detail, e.g: parcel and letter post records might not contain any commodity detail; cross-border surveys might provide data only at the higher HS levels (e.g., that of HS chapters); and commodities that are difficult to classify might be allocated to a few broad categories in non-customs sources, making it difficult to merge them with the more detailed customs data (see the example of Uganda’s Informal Cross Border Trade Survey below);
(b) Some transactions might be subject to simplified reporting requirements at customs;
(c) There may be conceptual differences between sources: e.g., enterprise records might contain the country of purchase and sale but not the country of origin or last known destination;
(d) There may be delays in data forwarding by some source agencies or these agencies may use different release calendars, which may lead to unsynchronized provision of data;
(e) There may be a risk of double counting due to overlaps in the information provided by different sources: e.g., between data on goods on consignment supplied by customs, and data on sales of the same goods reported by the controlling governmental agency;
(f) It may be difficult to organize data processing in an efficient manner, since source agencies may use different data submission media (hard copies, portable storage, electronic transmission, e-mail, etc.) or incompatible computer data files (the integration of different hardware and software systems is a problem in numerous cases);
(g) Data entry from certain sources (e.g., postal forms, passenger manifests) may involve the use of a disproportionate amount of time and resources;
(h) There is a need to cross-check data from complementary sources (e.g., customs and commodity boards) and to determine which sets are of greater reliability;
(i) Survey results that apply to a period longer than the reference period used for the compilation of trade statistics cannot be easily added to the customs data;
(j) It is not always possible to identify partner countries in detail and some rest categories will need to be used at times;
(k) The statistical value is made up of several components, some of which may not be available in some cases;
(l) In enterprise surveys, quantity information is frequently not collected, or cannot be provided at a level of sufficient detail.