What is a data catalog?
A data catalog is an interactive inventory of metadata that organizations will use to search, find, and understand enterprise data with the purpose of using, managing, or protecting it. A data catalog provides value for a variety of data and business roles including analysts, data scientists, and executives analyzing company data for business decisions, and also provides value for data teams including IT, data owners, and data stewards responsible for managing data.
Why does my company need a data catalog?
Consider your environment. Most data workers can relate to these statements:
- My complex data environment has become even more diverse with data living in various databases, on-prem and in the cloud, and in different formats.
- My company already has a lot of data, and data volume is constantly expanding.
- Data culture is growing and my company relies on data-driven decisions, so there is an increased demand for data.
- Data users in my organization don’t always know where to get the right data for analysis and know what data to use.
- My company needs to protect private data for security and for regulation compliance.
In all of these cases and more, a data catalog will solve these problems by creating a single source of truth to create a record of all of the various data in the environment with context for shared understanding and collaboration.
How does a data catalog work?
Data catalogs do not store the physical data, but they store metadata, which is the data that describes the underlying data. Data catalogs make it easier and faster to find and manage data with confidence by displaying, and sometimes creating, metadata that helps a data user to more deeply understand the data so that they can make decisions about how to use or manage it.
Let’s consider a data worker who is searching a data catalog to find a table that contains information that they need. The basic metadata in the catalog could include the table and column names, the location of the database where the table is stored, and when it was created. That insight would be the first step to help the user search and find enterprise data, but the data worker would still need to do some additional work and exploration to know if that was the right data to use, what it means, and how to use it. Modern data catalogs are solving that problem by providing more insight to help find and manage data.
Add value to your enterprise data
Modern catalogs use ML and AI to provide even more insight to make them more useful. Beyond the technical metadata, machine learning data catalogs are now able to create more insight and context both for data usage and for data management. Creating metadata in a way that enables action is Active Metadata. Data becomes more valuable as more users can understand it for analytics or data science or data management. For example, a data catalog may provide a glossary definition of the data, show or recommend related datasets, and surface who the data owner is. A data catalog may provide insight to know if the data is good to use by showing a data quality score, or peer crowdsourced voting and collaboration. As data environments expand and evolve, data owners face the challenge to provide the most current descriptions and details for users to understand data. A machine learning catalog can provide automated profiling inside the catalog for users to have a quick overview of the data to get a better understanding of the underlying data.
Reduce data risk
Data is an organization’s most valuable asset, and it is at risk of being misused or overexposed. Enterprise data becomes less risky when they can apply data governance at scale. Organizations reduce risk by adding context and understanding in a data catalog for correct and consistent use. A data catalog can also protect against the risks of overexposed data and compliance with privacy guidelines. Adding insight to a catalog view allows data teams to monitor, assess, and take action to correct any data that is at risk or is affected by privacy regulations.
What features should I look for in a data catalog?
A data catalog should provide an interactive view to find and search for data for the purposes of data use and data management. Organizations who care about data need to consider a comprehensive checklist of functions when evaluating data catalog options in the market.
Some data catalogs specialize in a single data source or a limited collection of data sources. Organizations that want to catalog data from multiple data sources and types, or across various platforms, should consider the breadth, variety, and scale of objects that a catalog will ingest.
An organization planning for future growth of a diverse ecosystem will evaluate a data catalog that will meet the needs that they have today and also be relevant as the organization evolves. Some basic catalog requirements include the ability to:
- Ingest essential data
- Search for data objects
- Connect to current business-critical solutions
- Integrate with current business processes and platforms
- Add insight and intelligence to promote data use and governance
- Plan for future growth
A high-value data catalog will guide data users to find data that they need, provide additional insight to better understand and select data for analysis, apply machine learning for deeper insight with automation to reduce manual tasks, and enable action for data governance.
Benefits of BigID data catalog
Applying patented machine learning, BigID’s Data Catalog delivers unique insights for context and visibility. Enable data workers to search a catalog with scalable classification and context for increased data understanding – with apps to take action on top of that catalog, from data stewardship to data quality to data retention and beyond. Deliver value to all data workers, with different views for data stewards, data analysts, data scientists, business analysts to have context for data use, and views for IT, security, privacy for data management.