How Data Products Facilitate the Right Data Quality Checks
Data quality is a term that resonates deeply within many organizations. However, its broad scope often results in varied interpretations and approaches. To shed light on this topic, Collibra has crafted an insightful article titled “The 6 Dimensions of Data Quality”, which delves into the multifaceted aspects of data quality.
To better explain the relationship between data quality and data products, I’ve created a barebones schema inspired by the key insights from Collibra’s article. This schema illustrates how specific data quality dimensions align with the essential components of a data product.
At the heart is the concept of a data product. Defined as “the read interface to a dataset”, a data product serves as a data provider.
The data quality aspect of the dataset itself emphasizes the accuracy of the data, viewed from the perspective of the data producer. The interface then presents the dataset to the consumer, with the consumer’s focus being on data availability. Note that the accuracy of the dataset is intrinsically linked to data availability; for instance, a certain level of accuracy might be necessary to make the data consumable.
The diagram adeptly highlights the synergy between data products and the various facets of data quality. It shows the crucial role data products play in ensuring data accuracy and in rendering datasets both accessible and usable for consumers.
In my role as a data architect, I use this diagram to guide discussions and shape data (platform) strategies. It serves as a visual aid that highlights the importance of data products in the context of modern data architecture, particularly in addressing the data quality concerns every organization has.