Today, at a time when data catalogues have become an essential tool for the enterprise data management; enterprises that successfully carry out data catalogue implementations are witnessing better speed, accuracy and quality in the data analysis and better engagement in the people concerned with data analysis. A data catalogue essentially stores data about the data, in other words metadata, helping businesses in the fast retrieval of the required information. By recording all the databases and files along with relations between the files, data catalogue serves as a quick means for the access of data. However, over time the enterprise data catalogues could become outdated, particularly at the time of acquisitions the metadata can become out of data in manner that can never be trusted for key business operations.   

Automation of Data Management

“Most organizations use only a small percentage of the data they have access to—in my experience less than 5%, even though they continue to collect and store terabytes of data. Many retail businesses have been collecting data on customer transactions and behaviours online but are only marginally using the gathered information to create more personalized customer experiences,” says Shervin Khodabandeh of Boston Consulting Group indicating the disconnect of businesses with data. Artificial Intelligence with its immense cognitive capabilities helps to address the challenges in the enterprise data management with intelligent data catalogues. Machine learning algorithms can easily automate the collection of metadata tags thereby allowing businesses to keep the data catalogues up-to-date all the time. The new generation AI-powered intelligent data catalogues incorporates automation into the data management and helps enterprises get the most out of their data.

These data catalogues enables businesses realize the potential of their enterprise data through unified metadata view that includes all key identifiers such as technical metadata, user annotations, business context, data usage, quality etc. They also help to uncover the complex data relationships which would not be possible otherwise and simplifies the data analysis of vast data gathered from multiple channels.  AI-powered data catalogues can also help to tackle the challenges in managing the ever-increasing and multi-source big data.