Ideally, in data warehouse administration, there is metadata which refers to information that underlies the fabric of a given source of data. Metadata is defined as an answer about the data in the data warehouse. It is important for administering and building your data warehouse. Metadata is significant for the given development and deployment of the data warehouse in a joint effort between the IT staff of the organization and the users. Metadata is the driver and provides a record for each process that is conducted within the scope of the data warehouse administration functions (Pasyeka & Pasyeka, 2016). It is therefore defined as the technical IT issue that is placed in a key position to enable communication among various processes that take place in data warehouse administration. There are three types of metadata that are important in data acquisition. Data acquisition processes involve data extraction, transformation, cleansing, integration and finally staging (Venkatasubramanian et al., 2017). The types of metadata that are important in the data acquisition process include source system platforms, target physical models, summarization rules, target logical models, data structures and staging area, source system logical models, source-target relationships, external data definitions, data transformation rules, source structure definitions, data cleansing rules, data extraction methods, source system physical models and external data structures (Hira & Deshpande, 2018). All of the above types of metadata have proved significant in aiding the listed processes involves data acquisition in the context of a data warehouse.

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