A data cataloging creates and maintains a directory of data stocks by identifying, describing and organizing distributed data sets. The data catalog provides a context that enables data agents, data / business analysts, data technologists, data experts and other line of business (LOB) data consumers to find and understand relevant data records and thus derive business benefits from them. Modern machine learning-enriched data catalogs automate various tedious tasks involved in data cataloging, including metadata search, ingestion, translation, enrichment, and the making of semantic relationships between metadata. Such next-generation data catalogs can therefore drive corporate metadata management projects forward,
Data catalogs simplify metadata management tasks and ensure that data consumers are better able to make optimal use of the data available to them.
What is Data Cataloging?
Data cataloging is the process of creating a planned list of your data. Once you’ve completed your data mapping process, use the Data Catalog (think of a map catalog in a library) to index where everything is stored.
It uses metadata (also known as data about your data) to collect, label, and store records. Your data sets can be stored in a data warehouse, data lake, master repository, or other storage location. Most companies choose to use cloud storage for their data.
The most significant benefit of having a well-organized data catalog is accessing the information it gives you. Your data is now correctly labeled and easy to find. With a data catalog, you can view all available data sets, quickly identify what you are looking for, and evaluate and analyze it efficiently and safely.
With proper data cataloging, you get transparency for all your data and a central data source for all your data warehouses. When your business needs to analyze and utilize an ever-growing data warehouse, you essentially need a data catalog.
How to Set Up a Data Catalog?
The first step in cataloging your data is to collect your metadata, including tags, files, labels, and tables. It is what your data catalog will consist of (it will not store the actual data). You can configure the software to explore your databases and collect this data from places like your data warehouses, cloud-based arrangements like AWS, data warehousing platforms like Hadoop, and other solutions. BI, transactional databases that use SQL, and those that use NoSQL like MongoDB.
Data analysts and business users also recognize the value of data glossaries. These less technical users grow the ability to calculate the application of a specific set of data without digging too deep. The Data Catalog then provides context to the dictionary, with its enhanced automation, discovery, and classification capabilities.
The next step is employing a BI platform such as Sienese to provide you with more thoughtful ways to network with your data. You can succeed and add to your data catalog right in the BI platform.
Types of Data Catalogs
When it comes to organizing big data, there is no one-size-fits-all approach. Gartner identifies three different subcategories of data catalogs to help you determine which type is most appropriate for your business situation:
• Tool- or supplier-specific data catalogs
These data catalogs can be deployed as a cloud-based data lake, data prep tool, or Hadoop distribution. This method requires little input from the organization, but it has its limitations in that you can have multiple catalogs of data as your supplier list grows. It makes it more time-consuming to log into a BI solution and set up your single source of truth.
• Data catalogs especially for data lakes
Data scientists and data engineers mainly use this type of data catalog. While this use case is extensive, it has limited customization across the Wrike Enterprise Plan. It does not allow business users to easily access the data and use it for their digital initiatives.
• Company data catalogs for analysis and teamwork
Gartner defines them as “general, business-oriented data catalogs for broader use in information governance and infonomy – intended for the chief data officer (CDO/
With a clear data catalog, you have a cleaner, faster, and more transparent analysis at your disposal. Your data catalog should enable your employees to gain better insight into the data and quickly make intelligent decisions. It places your business on the right track to be truly data-driven.
Benefits of The Data Catalog
- Improved data processing efficiency.
- Improved data context
- Reduced risk of errors
- Improved data analysis
The benefits of managing data from the data catalog become apparent when you consider the value of metadata and the possibilities that rich metadata opens up. However, the greatest value is often seen in influencing analytical performance. We operate in the age of self-service analytics. IT organizations cannot provide all the data required by the growing number of people analyzing data. However, modern business and data analysts often work in the dark, having no idea of the existing data sets, the content of these data sets, their quality and usefulness. They spend too much time searching for and understanding data and often re-creating existing data sets. They often work with inappropriate data sets, leading to inappropriate and incorrect analyzes. Figure 2 shows how analysis processes change when analysts work with the data catalog.
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