Data lives anywhere in the digital world. As such, enterprise data catalogs are invaluable assets in today’s digital businesses. Data catalogs help companies prepare for the GDPR, improve data quality, and enhance self-service data governance. Data cataloging technology has evolved to keep pace with the ever-growing needs of users. Below are ways a data catalog benefits its users.
Data Protection
Companies are using data catalogs to protect and govern their sensitive data. Look for a directory that can support granular data security as well as data masking. That way, it’s easier to protect critical data against intruders.
Enterprise Scalability
Data catalogs that are built on cloud infrastructure enable an enterprise to scale its services quickly and faster. In fact, data analysts can use directories to manage a vast pool of data sources including structured and unstructured data sources. Moreover, one can use data catalogs to manage data residing anywhere. In short, catalogs scales up with your growing data needs.
Crowdsourcing
Analysts use data catalogs to provide information about data sets, add comments, and rate data sources. However, users should add new, incremental scans to update existing metadata. Data cataloging is vital for business information that is subject to compliance policies.
Analysts use data catalogs to provide information about data sets, add comments, and rate data sources. However, users should add new, incremental scans to update existing metadata. Data cataloging is vital for business information that is subject to compliance policies. Moreover, data catalogs allow the use of electronic populated tags to review sensitive data sets. In fact, AI-powered directories will enable the use of machine learning technology to improve the accuracy of automated tagging.
Automation
Automated data catalogs allow users to load and scan metadata gathered from a vast pool of data sources. In fact, digital catalogs use machine learning and artificial intelligence technologies to tag, populate, and profile objective metadata intelligently. That helps improve the quality and authenticity of the gathered data. Automated data catalogs allow documentation, annotation, rating contribution, versioning, and crowdsourcing. In fact, data analysts can use roles-based policies to assign data to stewards, data sources owners, and end users. Moreover, it’s easier to access a well-governed and audited directory. Data catalogs provide solutions that allow users to search and reuse their existing datasets efficiently. Beyond this, data catalogs enable end users to keep track of end-to-end data lineage.
Easier Integration
Companies can use data catalogs to integrate their existing business lines with open APIs : (check out this list of free APIs for developers). You can also use data catalogs to incorporate your business processes to data discovery intelligence tools. Moreover, data catalogs that use search engines such as Solr allow enterprises to scale up as their user needs grow. That way, it’s easier to search the directory and make data-driven decisions.
Data Lineage
Data analysts use data catalogs to identify the origin of a data set. In fact, data catalogs usually discover and suggest missing lineage automatically to make it easier to address data lineage gaps. That helps eliminate missing data lineage chains caused by the manual entry. You will need a catalog to build new initiatives on consent management applications, data governance, data security, and data rationalization. The use of data catalogs enables analysts to adapt to changing data compliance policies. You should ensure a data catalogs vendor supports all these use cases.
While there are so many solutions that resemble data catalogs, only a few of them fulfill the requirements of a catalog. In fact, you will come across solutions that you can integrate with a real directory. Data catalogs should help data users and analysts to search common data definitions and data sources. It should also enable an enterprise to gather data definitions from classifications, automated data discovery as well as entity mapping. Data analysts can use complex automated algorithms or analyze data values to automate data catalogs population. Alternatively, data analysts can use APIs to collect metadata from stored procedures, views, or tables. Over time, digital cataloging solutions have become a necessity in addressing modern data management needs. A catalog is no longer an option, but an essential component that an enterprise can’t ignore due to data compliance regulations.
