What is a data catalog?
A Datenkatalog is an interactive inventory of metadata and data that organizations use to search, find, and understand enterprise data with the purpose of using, managing, or protecting it. They also provide value for a variety of data and business roles including analysts, data scientists, and executives analyzing company data for business decisions, and data teams including IT, data owners, and Datenverwalter responsible for managing data.
Does my company need one?
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 für Sicherheit and for Einhaltung von Vorschriften.
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.
Data catalog vs data dictionary
Data dictionaries are exactly what they sound like — a resource containing detailed information about your data. This can include descriptions of a variety of data attributes and fields. Data dictionaries are a powerful resource for IT team members, data officers, and developers, especially those looking for information regarding properties such as data type, length, valid values, correlations and much more.
Data dictionaries define each attribute or metadata category in the form of a spreadsheet with multiple rows and columns. This provides clarity for IT teams to gather information quickly and assess the actions that should be taken.
Data catalog use cases
Today’s organizations manage more amounts of data than ever before. For this reason, data catalogs have become an increasingly popular method of data management. Here are a few uses:
Make your data have impact
Ideally, the effort put in to collect and process your data will be rewarded tenfold. Data driven decisions are undeniably beneficial for businesses. Poor decision making can have damaging costs. Data catalogs can help prevent this loss by facilitating collaboration between teams and offering clear workflows.
Time efficient data processing
So much time is delegated just to finding the right data, and even then it might not be as useful as you hope. A data catalog provides crucial context to your data, saving valuable time and effort. They often detail the characteristics of data, value distribution, statistical information, or something as important as Persönlich identifizierbare Informationen (PII) or Personal Health Information (PHI).
Stay compliant with data privacy and protection regulations
One of the inescapable realities of data lifecycle management is adapting to new regulations. The ability to appropriately discover and label your data is the only way to stay compliant. Organizations that are able to demonstrate clear understanding of where your data is coming from, what it’s being used for, and who has ownership over it as it moves through the pipeline. Catalogs give organization to otherwise, unstructured and confusing data.
When used appropriately, a data catalog can:
- Lower total spend
- Increase operational efficiency
- Improve customer experience
- Decrease the risk of fraud
- Provide a competitive advantage
How does a data catalog work?
Data catalogs do not store the physical data, but they store Metadaten, which is the data that describes the underlying data. Instead, they 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 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. Some may provide a glossary definition of the data, show or recommend related datasets, and surface who the data owner is. It may also 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.
Datenrisiko reduzieren
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 catalog for correct and consistent use. It 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 should a data catalog offer?
A data catalog should provide an interactive view to find and search for data for the purposes of data use and Datenmanagement. Organizations who care about data need to consider a comprehensive checklist of functions when evaluating options in the market.
Some will 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 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.
Leverage your data with BigID
BigID’s data catalog provides a complete registry of data assets with context to increase data value and decrease data risk.
5 Reasons customers choose BigID data catalog:
- Enables data governance from a single platform to reduce complexity, break down data silos, and deliver consistent management.
- Includes both structured and unstructured data assets from any data source to manage all data in a single platform.
- Automatically populates the catalog from data scans, avoiding manual catalog management.
- Uses ML for advanced classification to add context at scale: identify what the data assets are, tag sensitive data with relevant privacy policies, and surface overexposed data.
- Extends data management benefits with native and custom apps including solutions for records management, data quality, and stewardship, with workflows and collaboration.
See how BigID provides discovery and classification at scale to enable data understanding and protection, in a 1:1-Demo mit unseren Experten für Metadatenmanagement.
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