Data catalog has been a buzzword for quite a while now. 451 Research has even stated that “There is a case to be made that the data catalog is the most important data management breakthrough to have emerged in the last decade”.
Why is Data Catalog so important?
Data catalogs have quickly become a core component of modern data management.
Organizations struggle to maximize the value derived from their ever-growing volumes of data, the focus is no longer on having data, but on knowing your data to break the 80–20 ratio between time spent in searching data and doing data preparation versus real analytics and decision making.
So now comes the question , What is data catalog?
Well 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.
This solves the above mentioned problem and makes one understand how critical it is to have a data catalog.
Now another question arises how is it better than former metadata management approach?
Organizations are struggling to get and maximize the value from its data, and the following are three main reasons for this that explain why data catalogs have been emerging:
Data proliferation: Your organization has never managed so much data, and more data that is spread over multiple locations.
Regulatory pressure: Your organization is now heavily scrutinized by industry, state, and national regulations that are asking for transparency and accountability.
Data democratization: Your data consumers are requesting more and more data, but at the same time they want to know where it comes from, and how reliable it is. They ask for the end of tribal knowledge and the advent of data democracy.
A modern data catalog includes many features and functions that all depend on the core capability of cataloging data that is ,collecting the metadata that identifies and describes the inventory of shareable data.
It is not very practical to catalog the data manual, automated discovery of dataset 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.
