Data Inventory

The need to have appropriately in place data inventories started to increase with the rise in complexity in current data pipelines and data platforms that require storing data in repositories of different nature such as data warehouses and data lakes. The data assets tracked in data inventories can be databases with associated structure, metadata, raw files, etc. These data assets are more scattered throughout various locations and resources, creating complications when an organization needs to find and query data assets.

Data Inventories hold information about names of data assets, nature of the contents, metadata on update frequency, intended use, owner, security and privacy standards apply, source, and other relevant metadata that might be necessary to operate with the asset. Ideally, data inventories should hold as much information about all data assets within an organization. However, it can be beneficial to narrow down data assets according to a predefined standard of importance and set clear policies stating how these data inventories are being kept up to date to account for changes in data assets.

Data Inventories are valuable tools for applying analytics to organizational data and reducing the risk of data breaches and exposures. Data teams can use it to create checklists to validate the security compliance of different data assets and to be able to be accountable for the quality of the data which is served to the various teams within an organization. It can also be used to design efficient reporting tools, support decision-making in the planning or design of data pipelines, and be used for overall performance optimization in data products. Data engineers can also flag existing data and missing data that needs to be collected or produced. Data inventories create a clear picture that organizations can use to develop roadmaps towards specific desired features or outcomes.