Traditionally, IT departments knew the data’s whereabouts, and business users knew what the data represented. This arrangement meant that neither of the parties knew enough about the data to benefit from its potential. The necessity of a data catalog stemmed from this line of control drawn between the data handlers and the business users.
You can primarily think of it as a reservoir containing information about all the data your organization owns – its source, its purpose, and the formulas it is part of. However, there is much more to modern data cataloging; we will get to all that.
A well-organized data catalog makes getting access to organizational data as simple as running a google search.
Jump to the detailed data catalog buying criteria hand picked by experts. (Link to the landing page)
In the early days, the tediousness of manual data cataloging put it out of currency; businesses gave up on it. Now, with the emergence of data lakes and the widespread use of automation, data catalogs have gained more importance than ever.
Data usage is no longer limited to IT professionals or data governance experts; the entire organizational hierarchy uses data to enhance each point of the customer lifecycle.
Data lakes have increased data collection and storage efficiency by allowing free data influx to form a large repository. The downside is that businesses often end up with a convoluted heap of information, difficult to consume or utilize.
Data cataloging tools tackle this problem by using automation to collect meaningful information about the data elements imported to the solution. It formulates a plug-and-play experience for business users.
Any data cataloging tool should cover two aspects of data management—metadata management, and of course, data cataloging.
Let us go over some of the capabilities offered by the best data catalog solutions in the business.
You can use these to build out the criteria for selecting a data cataloging tool. Or you can look into the detailed data catalog buying criteria we have created for you.
Contextualizing data: Putting data into context is vital for unlocking its analytical value.
Metadata analysis: A tool that can interpret metadata and suggest suitable titles can save a lot of time.
Auto adaptation to data environment: We have already talked about analyzing queries to build out catalog pages. It is useful when a tool can adapt to the changing data environment and hide or create catalog pages accordingly.
Machine learning capabilities: Machine learning lies at the core of modern data cataloging solutions. It does the heavy lifting in terms of data discovery, classification, and lineage analysis.
Metadata indexing: Scalable graph database architecture is used for metadata indexing.
You can connect your data analytics efforts to business outcomes. Having a data catalog on board helps you find the correct information to answer various questions related to the business.
It enables you to connect data flowing in from various data sources. Eventually, your organization gathers the data-driven insights that aids process enhancement and decision making.
Depending on your requirements, you can also look for capabilities like
Data cataloging helps you build a front door for your data environment. Incept can help you choose and implement a data cataloging solution.
Our data management experts have handpicked a comprehensive list of criteria(link to landing page) based on the features and services offered by the leading data cataloging solutions.