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Model Evaluation

Data Catalog

With the amount of data that is created on a daily basis, data catalogs have emerged as a means by which organizations can maintain a detailed, searchable inventory (leveraging metadata) that outlines all the data assets that they currently have at their disposal. 

A data catalog might include:


  • Structured (tabular) data: Data that would fit nicely into rows and columns such as customer data including names, addresses, and location. 


  • Unstructured data: Unstructured data cannot be nicely, logically structured into an Excel spreadsheet. This includes assets like photos, audio files, or videos. 

  • Reports: For example, collating internal financial reports into an all-in-one data catalog.

  • Data visualizations and dashboards: The results gleaned from data analysis—whether they are in numerical or visual form. 

  • Machine learning models: A data catalog can help organizations keep tabs on which data they used for which machine learning model. 


Data catalogs bring a sense of order to an organization's data. By ensuring that all data is accounted for and searchable in a single source, it is easier for data citizens (data scientists, business intelligence analysts, or any other employee looking to use the data) to find the data points they need for their use case or project. 

Data catalogs play a key role in the democratization of an organization's data, helping make the most out of it and fueling more accurate data-driven decision-making. This increases operational efficiency, reduces risk, and ensures that data science projects have a higher chance of success.

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