Sources: https://www.alation.com/blog/what-is-a-data-catalog/

The Significance of Data Catalogs
Data catalogs have quickly become a core component of modern data management. Organizations with successful data catalog implementations see remarkable changes in the speed and quality of data analysis, and in the engagement and enthusiasm of people who need to perform data analysis.

What is a Data Catalog?

A Data Catalog is a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness of data for intended uses.

What is Metadata?

Fundamentally, metadata is data that provides information about other data. In other words, it’s “data about data” It consists of labels or markers that describe information, making it easier to find, understand, organize, and use. Metadata can be employed with a wide range of data formats, encompassing documents, images, videos, databases, and beyond.

Exploring Data Catalog Metadata

Data catalogs have become the standard for metadata management in the age of big data and self-service business intelligence. The metadata that we need today is more expansive than metadata in the BI era. A data catalog focuses first on datasets (the inventory of available data) and connects those datasets with rich information to inform people who work with data.

Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource. People metadata describes those who work with data—consumers, curators, stewards, subject matter experts, etc. Search metadata supports tagging and keywords to help people find data. Processing metadata describes transformations and derivations that are applied as data is managed through its lifecycle. Supplier metadata is especially important for data acquired from external sources, informing about sources and subscription or licensing constraints. 

What Does a Data Catalog Do?

A modern data catalog includes many features and functions that all depend on the core capability of cataloging data—collecting the metadata that identifies and describes the inventory of shareable data. It is impractical to attempt cataloging as a manual effort. Automated discovery of datasets, both for initial catalog build and ongoing discovery of new datasets is essential. Use of AI and machine learning for metadata collection, semantic inference, and tagging, is important to get maximum value from automation and minimize manual effort.

1. Dataset Searching: Robust search capabilities include search by facets, keywords, and business terms. Natural language search capabilities are especially valuable for non-technical users. Ranking of search results by relevance and by frequency of use are particularly useful and beneficial features.
2. Dataset Evaluation: Choosing the right datasets depends on ability to evaluate their suitability for an analysis use case without needing to download or acquire data first. Important evaluation features include capabilities to preview a dataset, see all associated metadata, see user ratings, read user reviews and curator annotations, and view data quality information.
3. Data Access: The path from search to evaluation and then to data access should be a seamless user experience with the catalog knowing access protocols and providing access directly or interoperating with access technologies. Data access functions include access protections for security, privacy, and compliance sensitive data.

What Changes When You Implement a Data Catalog?

Without a catalog, analysts look for data by sorting through documentation, talking to colleagues, relying on tribal knowledge, or simply working with familiar datasets because they know about them. The process is fraught with trial and error, waste and rework, and repeated dataset searching that often leads to working with “close enough” data as time is running out. With a data catalog the analyst is able to search and find data quickly, see all of the available datasets, evaluate and make informed choices for which data to use, and perform data preparation and analysis efficiently and with confidence. It is common to shift from 80% of time spent finding data and only 20% on analysis to 20% finding and preparing data with 80% for analysis. Quality of analysis is substantially improved and organizational analysis capacity increases without adding more analysts.