Your data science team must build a weekly, SKU-level demand-forecasting model for a global retailer. During data discovery you identify the following sources:
FRED's Advance Retail Sales series, published monthly at the national level for the United States.
Eurostat's Retail Trade Index, published monthly for each EU member country.
A commercial point-of-sale (POS) scanner dataset that provides de-identified, store-level, UPC-level sales every week under a paid license that allows redistribution of the model outputs.
Which statement best justifies the additional expense of licensing the commercial dataset?
Commercial datasets are automatically exempt from privacy regulations such as GDPR or CCPA, so they reduce compliance overhead compared with public data.
Its weekly, store- and UPC-level coverage provides the temporal and spatial granularity required for accurate SKU-level forecasts, which the public datasets lack.
Using a commercial dataset removes the need for data cleaning or validation, shortening the development timeline relative to public data.
Public statistical datasets generally prohibit commercial model building, whereas commercial licenses always grant unrestricted downstream use.
The forecasting task requires weekly predictions for individual SKUs at specific stores. Neither the monthly, nation-level FRED series nor the monthly, country-level Eurostat index offers the temporal or spatial resolution needed to train such a model. The commercial POS scanner data supplies weekly, store-level, UPC-level observations, giving the coverage and granularity necessary to capture short-term demand shifts and regional heterogeneity, which directly improves model performance. The other statements are incorrect because commercial data is still subject to privacy laws, public data often permits commercial reuse with attribution, and all raw data-commercial or public-still require cleaning and validation before modeling.
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Why is temporal and spatial granularity important for demand forecasting?
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What are GDPR and CCPA, and how do they relate to commercial datasets?
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Why do even commercial datasets require data cleaning and validation?