Data catalogs are relatively a core component of data management tools. Thru this, automatic metadata management were enabled and molds with user-friendly interface that makes data easy to understand even for non-IT members of a business organization.
Metadata is the core of data catalog. It is the data that provides information about other data. To simplify, it is “data about data”. Many distinct types of metadata exist, including descriptive metadata, structural metadata, administrative metadata, reference metadata, statistical metadata and legal metadata.
- Descriptive metadata is descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords.
- Structural metadata is metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials.
- Administrative metadata is information to help manage a resource, like a resource type, permissions, and when and how it was created.
- Reference metadata is information about the contents and quality of statistical data.
- Statistical metadata, also called process data, may describe processes that collect, process or produce statistical data.
- Legal metadata provides information about the creator, copyright holder, and public licensing if provided.
Every organization seeking competitive advantage uses data catalog to turn big data into actionable insights. Data transforms meaningful customer insights to improve outcomes. In today’s rapid pace, managing massive data amounts is doable with the use of such data governance tool.
A tool where everyone in a business can find the data needed for collaboration is essential. 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 considered impractical to attempt cataloging manually. Automated discovery of datasets, both for initial catalog build and ongoing discovery of new datasets is critical. The 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.
With robust metadata as the core of the data catalog, many other features and functions are supported. The most essential functions includes:
- 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.
- Dataset Evaluation—Choosing the right datasets depends on the 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.
- 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.
A robust data catalog provides many other capabilities including the need for data curation and collaborative data management, data usage tracking, intelligent dataset recommendations, and a variety of data governance features.
Benefits of a Data Catalog
- Improved data efficiency
This helps the business more cost-efficient in deriving effective data-driven decisions to boost performance thru real-time data analytics.
- Improved data context
Gaining better data understanding to put things into perspective is a great way to visualize circumstances that surrounds each metric. With this, the context turn facts into actionable information leading to a well-informed decision for a positive business impact.
- Reduced risk of errors
The manual processing of errors is time consuming and can cause massive data confusion. Whereas with investing in data governance, an organized and improved inventory is presented with high quality and confidence.
- Improved data analysis
Correct data and analysis is provided for deeper, more informed insights. The rich context captured of enterprise data, including relationships between data sets, analyst usage & trusted comprehension is highly accurate.
Overall, data catalog dramatically improves the productivity of analysts, increases the reliability of analytics, and drives confident data-driven decision-making while empowering everyone in your organization to find, understand, and govern data.