**This is a sponsored post which I will be compensated for**
A data catalog is essential to business users because it synthesizes all the details about an organization's data assets across multiple data dictionaries by organizing them into a simple, easy-to-digest format.
Thus, an essential component of an Amazon S3-based data lake is the data catalog. The data catalog provides a query-able interface of all assets stored in the data lake's S3 buckets. The data catalog is designed to provide a single source of truth about the contents of the data lake.
Any entity that is comprised of data a data asset may be a system or application output file, database, document, or web page. A data asset also includes a service that may be provided to access data from an application. For example, a service that returns individual records from a database would be a data asset.
Data catalog tools helps dramatically improves the productivity of analysts, increases the accuracy of analytics, and drive confident data-driven decision-making while empowering everyone in your organization to find, understand, and govern data.
Data catalogs are soaring as organizations continue to struggle with finding, inventorying, and analyzing vastly distributed and diverse data assets. Data and analytics leaders must investigate and adopt ML-augmented data catalogs as part of their overall data management solutions strategy.
The benefits of data catalog tools are easier, faster search capabilities: These lead to better, more accurate, reliable analytic assets. Businesspeople can quickly find their needed information and get better business insights. This leads to more trust in the environment and therefore, higher adoption rates of analytic assets. This also enables first-time and future users to ramp up quickly.
Better compliance with internal policies, like security and privacy, and external regulations, like GDPR and CCPA: AI and ML capabilities can detect “sensitive” data like HIPAA or PII fields while usage tracking can determine potential access or illegal usage patterns and create an audit trail for compliance.
Cost savings: A significant amount of effort and time is spent creating analytical assets that already exist. Business users often reinvent the wheel when they can’t easily find a report. Data scientists create redundant data sets when their existence is not readily obvious. The inability to find what you want causes wasted time and effort in addition to redundancy of data and data assets. These all cost significant amounts of money. A data catalog highlights redundancy and inconsistencies and supports streamlining this overly complex environment.
Collaboration and annotation features: Context is critical to understanding and trusting these assets, making collaboration and annotation significant capabilities in data catalogs. These provide mechanisms for business people to determine if the analytical asset is appropriate for their needs, to find like-minded people creating assets they need, to form collaborative units with these like-minded people.