Data curation ensures data relevance for a specific purpose or audience. In practice, the curation process involves selecting, cleaning, enriching, and transforming data to meet the requirements of a particular project or application. Curation may involve data normalization, filtering, or aggregation to ensure that the data is fit for purpose and aligned with the client's specific use case.
In the context of data-as-a-service (DaaS), commercial data providers can curate their data offering by offering slice-and-dice products tailored to the unique needs of their client. For example, a commercial data provider may offer a large dataset of consumer purchasing behavior. However, a client may only need to analyze data from a specific region, product category, or time period. A DaaS company could curate the data by filtering and selecting the relevant data, and presenting it in a format that is easy to analyze and integrate into the client's system. Data curation enables commercial data providers to offer more targeted and customized data products that meet the specific needs of their clients.
By curating their data offering, providers can create value-added services that go beyond raw data access, such as analytics, data visualization, and insights. Hundreds of providers listed on Datarade Marketplace offer curated data products spanning geospatial data, consumer data, company data, financial market data, and hundreds more.