A data catalog is a tool for organizing all data assets in a company’s data landscape. It includes definitions, descriptions, ratings, responsible persons, etc. It also helps you find duplicate and similar data to facilitate data labeling, governance, and consolidation in your data landscape.
When your data environment becomes more complex, you need a system to organize and manage data. A data catalog helps with data management.
You can think of a data catalog as a tool to organize all the data assets in a company’s data landscape. It includes definitions, descriptions, ratings, responsible persons, etc. Thus, it simplifies research and data management.
When you have a data catalog, you can easily find the data you need. But the truth is that building a data catalog is not that simple.
Build a data catalog
Usually, metadata forms the basis of a data catalog. So you can consider the metadata that data about your data. And that’s what fills your data catalog. But how will you collect the relevant metadata?
One way is to have a subject matter expert or professional service provider manually review your entire BI landscape to organize data into spreadsheets, eliminate duplicates, and resolve conflicting metadata. , then use the results to build your data catalog.
It’s about finding a way to organize your data assets but also a way to build a data catalog.
Take advantage of automation
The metadata of your data resources is distributed across the various tools in your BI environment. It includes ETLs, databases, analysis and reporting tools.
Data tools are often siloed, resulting in only part of the data picture being displayed, even if your company has documentation for each data asset.
Using an automated catalog solution is the ideal solution for data management. It breaks down barriers between data silos. As a result, it automatically gathers metadata from across your entire BI landscape and integrates it into a cohesive form, enabling effortless use by business and IT departments.
With metadata, you can add, delete, and update data frequently. However, it will be a long process without automation.
With an automated data catalog platform, you can periodically check the metadata of all data assets in your BI landscape and update your data catalog.
Other things to consider:
1. Discover your data
Usually sources or data types constrain most metadata catalogs which limit their capacity. So expand your enterprise data catalog to encompass all data sources and data types for a complete view of your data environment.
Then you can use a tool like BigID to analyze all data from all data sources and apply ML to catalog, classify, correlate and analyze clusters to get insights.
Now let’s understand the following relevant terminologies:
2. Data catalog
It manages technical, business, and security metadata across the entire data ecosystem in a single view.
It also helps preview sensitive data, so you can determine overexposed and overprivileged data, in addition to identifying duplicates and originals. A data catalog also helps you filter data by type.
With classification, you can automatically identify, classify, and categorize data, metadata, and documents in any data source or data pipeline.
Typically, the classification function is based on data type, sensitivity, and regulation.
With the correlation feature, you can find all data related to an entity, uncover dark data, and identify related data.
5. Cluster Analysis
Data cluster analysis helps you find duplicate and similar data to help label, govern, and consolidate data across your data landscape.
Moreover, you can find structured and unstructured data through cluster analysis.
6. Automated tagging for context
You must manually tag most data catalogs. And data users outsource it. You can get rid of these issues by using a tool like BigID.
Its metadata exchange improves scalability, speed, and accuracy using ML.
The tool also adds the data context, which helps you know the data. It also lets you automate the labeling of datasets, eliminating the need for manual labor.
7. Data Privacy and Trade Policies
With the evolution of regulatory policies, the rules have also changed. Therefore, companies must maintain additional corporate data policies.
So it will help you if you use a tool, such as BigID. Its policy manager feature allows you to add, update or modify policy rules using templates or create your special rules.
8. Tagging Datasets
It will help if you label datasets with policies for insight, application, and action. However, be sure to align the data with the correct policy. Alignment should focus on business rules or sensitivity.
You need the following professionals to ensure perfect data management in your company:
1. Chief Data Officers
They can give you a complete view of all data in the environment with classification. As such, you can know the data and know how to use it.
2. Data analysts and data scientists
Data analysts and data scientists can choose the right data for analysis and modeling. They also provide information and context.
3. Data Stewards
They populate the data catalog with information and classification to increase data productivity. In addition, sorting helps identify and label data.
Now you know you need a system to manage your business data and how a data catalog helps with data management.