To conduct a comparative analysis between two data providers we need to validate several parameters.
1. The GTIN codes of the product scope.
First to be able to compare aggregates, we need the list of GTIN for all the products in this aggregate, in fact having transparent access to this GTIN list is a right.
Openhealth makes the GTIN code available for 2 reasons:
By transparency and to promote interoperability. The user of the HUB platform can extract data from their applications with the GTIN code
In order to know at any time the content of an aggregate (example: a product class) and to follow its evolution over time with full traceability.
Example: evolution of the content of a product class following the marketing of new products.
I draw your attention to the fact that an aggregate at OpenHealth can have the same name at another data provider, but this does not imply that its composition is identical.
2. The data source
order to be able to make the comparison between aggregates, other criteria are taken into account, in particular the data source: Pharmacy, Parapharmacy, etc ...
It is not the same to look at the sales data of mouthwash type products in PHARMACY and PARAPHARMACY, see in CONSOLIDATED (PHARMACY + PARAPHARMACY)
3. The geographic territory
Likewise the geographical territory is very important, at OpenHealth we have Metropolitan France, but also Corsica, Monaco, DROM-COM , international data (Germany, Switzerland, Italy, Portugal, etc)
And you will agree that looking at the market for solar products in the month of December in Metropolitan France compared to data from Réunion is not the same thing.
4. The time period
The time period is one of the parameters of the analysis, we must compare 2 identical periods. This makes it possible to erase the effect of seasonality, or the presence of a one-off atypism which can result in a social movement, for example the impact of the yellow vests which has had numerous economic impacts.
5. Indicators and calculation method
To complete the analysis we need to compare indicators, and for that to know the associated calculation method.
At OpenHealth, the calculation method for sales values:
It is not the same thing as calculating the sales values from a reference price, and this method allows us to know the price disparity by product, in particular via the sheet: Price Dispersion
5. THE LAG: The dates of the first sales and the date of registration
Finally, OpenHealth is able to provide for each of the GTINs within an aggregate, the date of the first sale as well as the date of registration of the GTIN in our repository. This is a very differentiating point, because we are able to intercept the sales data of a product even before it is created in our repository. These sales are then recorded and then once the product has been created, they are linked to its product throughout the history.
These two dates are very important to determine the lag between the moment when we intercept the first sales data of a product and the date of creation of the product in our repository.
The difference between these two dates allows us to determine and measure the lag, and thus deduce the period of validity of the data.