5 reasons why a data catalog and data quality work better together
Two fundamental parts of most data management solutions are data catalogs and data quality tools. A data catalog allows you to locate and keep track of all your data, while a data quality tool ensures data looks and works the way you want it to.
While data catalogs and data quality can work independently, there are many advantages when they seamlessly work together. Namely, a data catalog can lead to better automation of data quality tasks, making data quality management less time consuming. Read on to learn more about the benefits of a data catalog and data quality tool that are integrated and work together.
How a data catalog and data quality work together
A data catalog serves as a central access point connected to all your data sources. Data isn’t stored in the catalog; rather, metadata about your data is, which makes it easier to locate, scan, and understand what data is available across sources.
One of the best functions of a data catalog is that business terms can be assigned to data, either automatically or manually, sorting data into categories and giving it meaning. After that, the catalog’s AI-powered capabilities can locate similar data and automatically apply these business terms to it, as well as assign them to incoming data.
This is helpful for data quality automation because you can automatically run data quality rules on any data with a specific tag, instead of going through data sets and manually applying data quality rules to them. The catalog can automatically apply the necessary rules based on whatever business term you’ve tagged to the data, saving a lot of time and manual effort.
For example, let’s say you want to validate email addresses used by different departments. You assign several rules to the tag “email” in the catalog, and then it will automatically apply these rules to any data with that tag.
This automatic application of data quality rules based on business terms is one of the key benefits of using an integrated data catalog for your data quality initiative.
Key ways data catalog and data quality work together
When data catalogs and data quality tools are integrated, they complement each other in powerful ways. Below are five of the most impactful benefits:
1. Automating data quality monitoring
As a data steward responsible for the email data mentioned above, you need to monitor data quality issues related to email data in every possible business system. This is much easier to do with a data catalog because you have attributes and metadata readily available, so you can search for and find email data across systems. Also, without any configuration needed, the data catalog will recognize anomalies, such as unexpected changes in minimum average or sums for transactional data.
Beyond traditional data quality monitoring, the catalog surfaces data quality metadata. This allows anyone exploring or discovering data in the catalog to see its quality. Data quality information then stays up to date, together with metadata. One of the most common examples of automated data quality is data observability (see how it works here).
2. Improving data discovery
A data catalog and data quality tool can also work together to help you find the best data sets for your project. You’ll know which data sets you need by looking at the business terms applied in the catalog and then finding those data sets that are of the highest quality using data quality insights displayed in the catalog.
Perhaps the best part of the integration is that you can assess the quality of data you didn’t even know you had. Since the catalog can automatically apply business terms to data, you can run a data quality evaluation of all data tagged with a specific term and therefore evaluate data you didn’t tag yourself or maybe didn’t know about.
3. Streamlining on-demand data quality evaluation
Suppose you need to know the data quality of a specific data set that hasn’t been checked before. You can bring it into the catalog, click a button, and evaluate its data quality.
This also allows you to do evaluations in one tool instead of bouncing from a profiler to different data storage systems and working with isolated data quality management tools. You can quickly adjust terms and rules and apply them to attributes in the catalog.
4. Simplifying data preparation
When preparing data without a catalog, you would have to locate all the data you need and understand it on an attribute-by-attribute basis. The catalog allows you to find data of interest immediately and presents you with the metadata needed to decide what should be prepared and what kind of preparation is necessary.
You will also be able to work with the data sets that you find and transform them according to your needs. The catalog lets you find the data, evaluate its quality, and prepare it. For example, data profiling from the catalog might reveal that certain emails in your registry have the prefix “:mail:to:” attached. You can then run transformations to remove the prefix.
5. Helping discover root causes
One of the excellent use cases of a data catalog is root cause analysis by using data lineage. With data quality integrated into your data lineage, it will tell you information about your data’s quality throughout its lifecycle, from its creation to storage and its eventual use in your business.
You can use data lineage to trace problematic or low-quality data back to its source. Once you find what’s causing the drop in quality, you can take proactive steps to correct it.
Learn more about data lineage in this article.
Common challenges in combining data catalog and data quality
Bringing data catalog and data quality together has clear benefits, but it is not always a smooth process. Organizations often run into a few common challenges:
- Data silos: Different teams may use separate tools or maintain their own data sets, which makes it difficult to get a single source of truth.
- Solution: Break down silos with a unified platform and encourage cross-team collaboration.
- Lack of metadata standards: Without consistent naming and tagging, even the best catalog can become confusing.
- Solution: Define clear metadata standards and enforce them across all systems.
- Integration complexity: Legacy systems, outdated APIs, or incompatible formats can make adoption slow.
- Solution: Use tools that offer comprehensive and flexible integrations and automation to reduce manual work.
- Cultural resistance: Sometimes the challenge is not technical but organizational. Business teams and IT may not be aligned on priorities.
- Solution: Involve both groups early and demonstrate quick wins to build buy-in.
Tackling these hurdles upfront ensures that organizations can fully realize the value of data quality and integration with a catalog.
Best practices for implementing data catalog and data quality together
To make the most of your investment, it helps to follow some practical best practices when combining data catalog and data quality:
- Start with high-value data sets: Focus first on the data most critical to your business and driving outcomes. Quick wins build confidence and prove value.
- Involve both IT and business users: Technical teams ensure systems work correctly, while business users bring context to how data is applied.
- Standardize metadata and terminology: Agree on consistent definitions and labels to avoid confusion and improve discoverability.
- Leverage automation where possible: Automating tagging, monitoring, and rule application reduces manual effort and speeds up adoption.
- Establish governance and ownership: Assign responsibility for maintaining data quality and catalog consistency, supported by clear processes.
Bringing data catalog and data quality together
Integrating data catalogs with data quality solutions is essential to any successful data management project. Keeping them connected has numerous benefits, including time savings, reduced risk of errors and data quality issues, and increased data accuracy. Whether it’s data quality monitoring all in one place, making on-demand data quality evaluation easier, finding what data needs preparation, or discovering problematic data sources, it is all much more efficient and smooth when the catalog and data quality work together.
FAQ
1. What is the role of a data catalog?
A data catalog organizes and indexes all data assets in one place, making them easy to find, understand, and use. It helps teams quickly discover the right data for their projects.
2. How does a data catalog improve data quality?
A data catalog improves data quality by adding context, tracking lineage, and flagging issues. It ensures data is consistent, trustworthy, and ready for decision-making.
3. What are the benefits of combining a data catalog and data quality?
When used together, a data catalog and data quality solution provide a single source of trusted data. This combination boosts accuracy, streamlines compliance, and improves collaboration across the business.
