For a data catalog to function, it must collect descriptive information about all data. This is the metadata. The metadata later enables the user to find the desired data quickly and efficiently on the basis of certain characteristics. The data catalog accesses the customer's databases for this purpose. These can be CRM systems, ERP systems, data warehouses, data lakes, databases or a master data repository, for example. These can be stored either "on premise" or in a cloud and can be accessed either via a direct database connection, via APIs or via ingest databases.
In addition, the data catalog can also contain other data information types such as data reports with visualizations as well as APIs, data lineages and relationships between data. Basically, a data catalog distinguishes between two types of metadata:
Automatically extracted data: This metadata is derived solely from technical information and analysis of actual data sets, such as machine learning methods.
Manually added data: This metadata is usually in a business context and therefore cannot be extracted automatically. They must be manually added to the data catalog.
A data catalog is responsible for structuring and documenting data. To do this, the data catalog analyzes data sources based on metadata, tags, annotations, similarities, the respective context, or the data origin. It does not matter whether the data is already structured or still unstructured, or what type of data it is.
In analyzing the data for structuring, the data catalog makes use of modern IT methods: with the help of artificial intelligence (AI), machine learning (ML), semantic interference, tags, patterns or relationships, it succeeds in systematically scanning databases and automatically deriving the required information.
By classifying and linking metadata to terminologies and processes within an organization, business glossaries or data dictionaries can also be created to facilitate the use of the data catalog.
The centralized business and technical documentation of the data assets in the data catalog offers a decisive advantage: a "single source of truth", a central point of truth, is thus created within the company.
The data governance function is a core part of a data catalog. This function manages and documents user access to data. The data governance function assigns roles and permissions, identifies responsibilities for the data, and analyzes the quality of the data and the data flows. On the basis of functioning data governance, it is possible to adhere to the company's internal compliance guidelines while at the same time taking into account legal regulations such as the General Data Protection Regulation.
Info: 43% of analyses are held back due to governance concerns
Advanced data catalogs are characterized by extensive data analysis tools and thus provide the user with far-reaching options for further searching and analyzing data. For example, the data catalog can prepare and document data specifically for metrics, reports, KPIs or comparable evaluations. API interfaces make it much easier to output and evaluate analyses from the user's point of view.
The user interface of modern data catalogs is designed in such a way that it actively supports the user flow and offers an intuitive user interface with an integrated search function. To ensure that the data catalog can be flexibly adapted to changes and is largely scalable, it should have open interfaces to the outside world. This makes it possible to extract metadata to other applications or import data.