Data catalogs are inventories that allow users to discover, search, understand and manage data assets according to specific needs. Data assets are classified and filtered based on metadata and search attributes.
Intelligent Data Cataloging (IDC) Tools utilize artificial intelligence, machine learning and semantic understanding to organize datasets, expand data knowledge as well as simplify data access and resource sharing. They enable users to locate data efficiently from the continuously expanding data reservoirs.
Why is Data Cataloging necessary?
Data catalogs are the foundation of data governance programs, analytical insights, and resource usage. They expedite interdepartmental communication and resource sharing by unifying data and simplifying data recognition.
In recent years, digitalization has led to the collection of an abundance of enterprise data. Data management has become increasingly complex and cannot be overseen manually. As a business grows, it accumulates more data that soon becomes unmanageable and unyielding. Client and customer data, feedback, daily employee activity, business communications, market statistics, revenue data are a few of the types of data that is created and stored. This collection of data is a valuable possession that can be exploited to generate insights and improve business management strategy.
However, a large part of this data remains unexplored and unused because it is never discovered. Substantial amounts of data lead to a lack of awareness amongst users. Most users work with limited resources owing to the limitation of capability to explore data manually.
Data catalogs aim at solving this problem by utilizing metadata to increase visibility of data assets in multi-departmental, multi-platform data architectures. The data cataloging tools have embedded functionalities that compile data in a centralized location after categorizing, classifying, organizing, and profiling data meticulously.
Some features of Data Cataloging Tools as listed below, assist data managers to create directories of data assets and tap into the value and potential of data.
Features of Intelligent Data Cataloging Tools
Artificial intelligence, character recognition and semantic understanding are used to categorize data based on keyword density and topical references. This means that even when a particular keyword that is being searched is not available, the catalog will suggest related searches and show documents that are in a similar category.
In addition to document scanning, data cataloging tools also harvest metadata (information regarding the data asset). In collaboration with metadata management tools, they utilize tags and search attributes to organize data assets in a logical, easy to navigate manner.
Data value evaluation and prioritization
Documents and data entries are prioritized based on the metadata, keyword density and structure of documents. Value evaluation allows users to realize how relevant a data asset is to their current goals.
Data connection and data synchronization
When working with hybrid data architecture and multi-platform storage, data catalogs systemize and link data assets from different platforms and data silos to eliminate duplication. They unify the data in a central storage unit to smoothen data accessibility.
Data lineage creation and relationship management
Data lineage is the record of the journey of a data asset from its creation. The user related data, editing, and changes in storage location form the data lineage. Data lineage contributes to the organization and classification of data.
Data relationship refers to how and when data assets are used together and the correlation between them. Based on this information, users can identify which data assets have related information.
Business glossary integration
Data catalogs depend on the creation of a well-rounded business glossary. A business glossary is a directory of terminologies used frequently in documents. These terms function as search attributes. Data catalog tools utilize the existing business glossary to run searches and continuously add more terms as more data is added to the catalog.
Multiple other features like artificial intelligence driven insights, API creation and management, multi-cloud management and structured and unstructured data management add to the organizational capabilities of data cataloging tools. Comprehensive metadata and intelligent data catalogs enable enterprises to improve data management systems and, in turn, increase business intelligence and improve analytical outcomes.