Understanding your data with catalog tools

Catalog tools in Qlik Cloud help you improve your data efficiency while maintaining security and compliance standards. Access to critical business metadata and the ability to apply user-defined tags and classifications are essential for data analysts and business groups.

Data administrators can verify the accuracy of data content and optimize data discovery by assigning and editing business metadata (names, descriptions, tags, and classifications). Business Intelligence users and app developers can configure personalized tags, apply classifications, and browse dataset samples and profile statistics to ensure that datasets are easy to find and contain the right information.

Catalog tools are particularly useful for new and existing datasets that a user has access to in their personal and shared spaces. Users have views into data files that help them to gain insights and make determinations about the data. Users can see where the comes from, what type of data it is, and how it can best be analyzed and used. This information helps to determine whether to create a new app with data or whether to load the data into an existing app.

The following Catalog options are available in the Analytics activity center for data discovery and managing business metadata:

• Dataset overview: Business and technical information about the dataset can be reviewed here. Details include the source file type, space, creation and last modified timestamps, field and row count, creator and owner, usage metrics, applied tags and common data classifications. See Managing dataset metadata for a complete list.
• Tagging data for improved search: Tags are filterable metatags that assist with the organization and discovery of data. Users associate tags that can be searched for quick access and identification of relevant data assets.
• Data profile views and sampling: Catalog profiles your datasets with statistics such as name, data type, sample values, most common values, and value frequency, and number of distinct values. Users select profile statistics of interest to uncover trends and anomalies in the data. See Managing field-level metadata and data profiling for field data visualizations and a list of available profile statistics.
• Dataset classifications: Data governance standards vary widely across regions and industries. Most data processes are impacted in some way by the need to secure data by limiting access. User-configurable classifications pertaining to regional and global data privacy and sensitivity standards and policies are provided to identify datasets with custom and sector-specific categories. See Managing dataset metadata to learn more.
• Create an app from your data: Qlik's data-first approach presents an option to upload data files and gain insight into your data before creating an app. This is a key element of the raw to analytics-ready workflow. See Creating an app from data to create an app from data or see Adding data from uploaded data files to upload data files to an existing app.
• Viewing the lineage of your data: You can view a lineage graph of your data, showing the upstream origins of your apps and datasets. For more information, see Analyzing lineage for apps, scripts, and datasets.
• Viewing the impact analysis of your data: You can view outputs and see the downstream impacts of apps and datasets in the catalog. For more information, see Analyzing impact analysis for apps, scripts, and datasets.
• Data quality: When looking at a dataset or a data product, you can get an idea of the quality of the data they contain, from the overall quality and freshness, to the number of empty or invalid row for each field of a dataset. For more information, see Data quality and data discovery.
• Documentation of data products: Get information on what the data product is about, and who to contact in case of questions. For more information, see Creating data products.