What’s a Data Catalog?

A data catalog helps an organization create a comprehensive inventory of all their data assets spread across various systems and projects. Organizations often have their data distributed across multiple relational databases, data warehouses, operational databases, and legacy systems. A data catalog provides an efficient solution for an organization’s data discovery, analytics, and compliance requirements.

In 2020, Seagate did an industry study that revealed 43% of data that organizations collect goes underutilized. Why does this happen? The data assets of an organization often reside in silos. Only a few teams have the know-how to discover and analyze these data assets. The real issue is not the scarcity of data but the lack of a smart system to organize and present data. A data catalog provides an efficient solution by collating the metadata associated with the data assets.


Metadata The Foundation of a Data Catalog

A data cataloging tool crawls through all the data repositories of an organization and collects metadata. Metadata is the information that accompanies the actual data. It describes and annotates datasets. A data cataloging tool automatically collates metadata, understands data semantics, and infers data connections. A dataset has different types of metadata associated with it. They define various aspects of the data such as:

  1. The source/supplier of the dataset
  2. The content of the dataset
  3. The meaning of the tables and columns
  4. Where the data is stored and who can access it
  5. The history and lineage of the dataset
  6. The reliability of the dataset

Based on the aspect it describes, the metadata can be divided into three broad groups: technical metadata, process metadata, and business metadata. A data catalog utilizes all these types of metadata to create a unified view of the data assets.

  • Technical Metadata describes the structure of a dataset, so it’s also called structural metadata. Names and descriptions of data tables come under technical metadata. It also describes the columns in a data table and the business logic used to compute them. Technical metadata is useful for data discovery.
  • Process Metadata comprises the lineage of a dataset. It provides insights about the source/creator of data assets and the time of creation. It records the usage information — who has used a dataset in the past and when. The process metadata helps data analysts to determine if the data is recent and reliable. Process metadata is also known as administrative metadata.
  • Business Metadata is particularly helpful when an organization needs to make a data-based decision. It describes the quality and reliability of a dataset. It also shows if the data is certified.

An organization’s data assets might have rich metadata associated with them, but it needs to collate, analyze, and infer this metadata to derive value from it. This is the primary function of a data catalog. Along with automated metadata collection, a data cataloging tool also allows crowdsourcing of metadata—a process through which data stakeholders manually add metadata. It also facilitates data curation, through which a data owner can enrich the dataset by adding usage tips.

Major Functions of a Data Catalog

Data Discovery

In many organizations, data resides in silos, and only a few teams know about its existence. Siloes limit the ability of users to find data that might facilitate better decision-making. Data analysts might end up creating new datasets or rely on partial or unreliable data.

A data catalog solves this issue by providing a unified view of all the data assets in an organization. Most of the data catalogs offer a search-engine-like user interface where users just need to type in the keywords for the data they are looking for. The data catalog will then retrieve a list of data assets that match their keyword and search filters. Data catalogs can also provide Application Programmable Interfaces (APIs) to automate data discovery.

Data Assimilation

In addition to data discovery, data catalogs help users understand the data better. Using the technical metadata, a data catalog provides a complete description of the dataset. This means that a user gets deep insights into the meaning of a dataset and its business logic.

Data Quality Assessment

Data catalogs collate process and business metadata to facilitate data quality assessment. Based on the history and lineage of the dataset, users can decide if the data is fresh and reliable. Data catalogs allow crowdsourcing of metadata and manual data curation, which further enhances the quality of a dataset. A data catalog continuously evolves by incorporating reviews and tips from users. Therefore, a data catalog helps an organization to build trust in its data assets.

Data Acquisition

Once users discover a reliable dataset, they might want to acquire it for analytics. Data catalogs often make it easy to access and integrate data for use in analysis. In advanced data catalogs this is as easy as a push of a button, allowing access to the data in the desired tool or for download. Faster data access can ultimately shorten the time to gaining insights for decision making. A data catalog standardizes the data acquisition procedure.

Why Do Organizations Need a Data Catalog?

Explosion In Data Volume

An organization will likely generate or collect enormous amounts of data. The huge volume and complex distribution of data assets make it very difficult to even know if appropriate data needed for analysis exists. The lack of visibility to data resources across the enterprise makes it hard to use that data to inform decisions. Further the explosion of data makes it harder to find reliable data. As a result, employees might rely on no, partial, or unreliable data as it’s challenging to reach the right data. This results in the underutilization of the data assets. A data catalog helps the organization discover high-quality data, no matter where it lives.

Data Regulations & Governance Needs

When an organization owns enormous volumes of data, it becomes difficult to control and protect it. It might lead to inadvertent data leaks. With strict data protection regulations like General Data Protection Regulation (GDPR), organizations need to ensure only the right people have access to the right amount of data. A data catalog helps to control data access and facilitates data governance. With a data catalog, enterprises can put in place rich controls to ensure appropriate visibility and permissions exist around data resources. It also helps compliance officers to unearth potential security issues of a dataset.

Better And Faster Decisions

A data catalog collates information about the lineage of data. The lineage information includes the origin and usage history of the data. Data catalogs also allow manual curation of the data assets through ratings and reviews. Data curators can also add tips and tricks to use the dataset effectively. A data catalog helps the decision-makers in an organization to make well-informed decisions backed by reliable and high-quality data.

Decentralize Data Management

Data catalogs bring about a cultural shift in data management. Often, a few teams, including data analysts, scientists, and IT teams, manage and curate data. Data catalog turns this centralized data management paradigm into a community-based data curation process.

How Do Data Catalogs Assist People in Various Data Roles?

The data catalog is a versatile service that can provide a wide range of features to different data roles in an organization.

Data Analysts

A data catalog assists the analyst in quickly finding relevant datasets. As the data is appropriately annotated with its lineage clearly marked, an analyst can pick the right dataset from a range of options. The tips, reviews, and comments associated with data assets promote efficient data analytics.

Data Compliance Officers

A data catalog helps an organization ensure legitimate data access. Compliance officers can enforce authentication procedures using a data catalog. A data catalog also enables transparent data access. It assists the data governance roadmap of an organization. Data catalogs help organizations to conform to regulations like GDPR.

Data Architects and Strategists

A data catalog helps data architects to support the creation of a governed, self-service approach for authorized employees to discover, re-use, and share crucial enterprise data. A data catalog allows users to leverage a central tool to discover the internal data they need as well as meta data that helps them assess the quality and characteristics of the data.

Essential Features that a Data Catalog Should Support

Cataloging Data Assets

A data catalog should crawl through the enterprise data in data lakes, warehouses, relational databases, and file systems to automatically collect all the metadata and infer the connection between datasets. It should then use the metadata to tag the datasets. Besides collating datasets, a data catalog should also collate reports, wikis, and other forms of unstructured data assets.

Data Search Capabilities

A data catalog should provide a simple, natural language-based search facility. It should take in keywords or business terms and display the related data assets ordered by search preferences. The data catalog should also display search results based on the access level of the user and have data obfuscation features to mask data from unauthorized users.

Data Evaluation Capability

Once a user discovers datasets associated with a keyword or search term, a data catalog should help them evaluate the data. If the user has the right to access the data, the catalog should allow the user to preview the dataset, see its lineage and ownership, and certifications. A data catalog also should collate user ratings and reviews and display them to the user.

Data Security

A data catalog needs to support the data governance procedures of an organization. It should respect the data security practices and authentication procedures of an organization. It should also have the capability to enforce data security at different granularities — at a dataset, table, or column level.

Data Acquisition

Once a user discovers and evaluates a dataset, they need to acquire it. A data catalog should facilitate hassle-free data acquisition. It should be as easy to search for internal data assets as easily as one might do a web search. And when the data does not exists, the data catalog should establish a process by which the users can raise a request for the data asset.

Improving Data Quality

Along with data discovery, evaluation and acquisition, a data catalog should also help an organization improve data quality. The data catalog should show data conflicts and flag incomplete and unreliable datasets. Apart from automated quality control, a data catalog should also incorporate community-based quality control where users can rate a data asset and comment on its quality.

Manual Data Curation

Along with automated metadata collation and data tagging, a data catalog should also allow manual curators to enrich the data. A curator should be able to remove a dataset from the catalog if it appears unreliable. The curator should also be able to add keywords and tags to datasets, flag highly sensitive data, add additional metadata, and share usage tips for the data asset.

Crowdsourcing Capability

A data catalog should have features that enhance a community-based curation of the data asset. Users should be able to add metadata, rate the data quality and add reviews and tips. A catalog should make it easy for various users to contribute to the curation of the data assets.

Are You Looking For A Data Catalog? Let’s Get You Started

Alteryx Connect is a powerful tool that serves all your data cataloging requirements. It helps you discover your data and business assets, maximizing their utilization. It also helps your organization to collectively curate and enrich the data. With Alteryx Connect, you will be able to quickly create a trusted data catalog. Check out the Alteryx Connect datasheet today to take a crucial step in your data management policies and plans.