A data catalog inventories and makes critical datasets available through metadata management. This platform informs businesspeople about what dataset assets exist and are related, where to find them, when they appeared, who created them, and how to access them, among other insights.
As centralized repositories, data catalogs aim to be relevant to users across an organization, systematically organizing and presenting selected datasets and their contexts. That way, businesses can comply with regulation, security, and privacy practices while enriching catalog entries as new datasets or data relationships become available.
Corporations continuously curate data catalogs to make dataset searching and selection more relevant to users. These curation processes evolve to keep up with business and marketplace changes and are guided by Data Governance activities that formalize data roles and processes and handle metadata management.
Combining data cataloging with Data Governance aligns business units on meanings, processes, and prioritization around data assets. When organizations agree on data descriptions, employees and stakeholders can better use data catalogs to resolve access issues, and Data Governance sessions and outcomes have better success.
Data Catalogs Defined
Data catalogs are similar to business directories in that they help users find business terms or connect to business glossaries. However, these repositories go beyond typical directories by providing detailed metadata to understand datasets.
Also, data catalogs capture a 360-degree view of data assets owned across the organization and return semantic relationships of that data. Consequently, data catalogs provide a platform to share and discover otherwise hard-to-find datasets while allowing data stewards to remain in control of how to manage this information. Since data catalogs capture data assets across their organizations, they encourage better cross-departmental collaborations.
Additionally, the self-service aspect of data catalogs provides business users with an interface to make information searching and generating more visible, actionable, and manageable. Consequently, data catalogs offer professionals a familiar browser-like experience to search and discover relevant data to answer business questions and clarify workflows and processes.
How Do Data Catalogs Differ from Data Dictionaries?
Data catalogs and dictionaries are different but related tools. While data catalogs and dictionaries rely on metadata management and defining the meaning of the data, they serve very different purposes, audiences, and focuses.
Data catalogs have an interface for business consumers to search and retrieve relevant datasets. Additionally, data catalogs point to related datasets for a topic through profiling and tagging, and can retrieve lineage.
This functionality fosters sharing and discovery among professionals. Moreover, since data catalogs holistically capture enterprise data assets and require alignment to do so well, they rely on and encourage better cross-departmental collaborations.
On the other hand, data dictionaries serve technology staff needs in building, updating, or maintaining a Data Architecture. A data dictionary provides technical metadata about data structures so that engineers can ensure proper data creation, updating, transformation, delivery, usage, and deletion.
A data dictionary may provide a building block to a data catalog to, at the very least, identify what entities exist in the computer and give a basic description. APIs use the data dictionary to configure and run services. Furthermore, a data dictionary allows technical personnel to quickly identify anomalies and errors, improving the Data Quality in the data catalog.
So, while data catalogs present a user-friendly interface for businesspeople to locate and get datasets, data dictionaries provide technical instructions to engineers. Key distinctions between the two occur in the primary audience, purpose, and depth of technical versus business-oriented information they provide.
What Is the Function of a Data Catalog in Data Governance?
Data catalogs play a primary role in Data Governance, functioning as a deliverable and a tool to stimulate conversations and agreement around critical data entities and their relationships. In this process, organizations get to a single source of truth, a repository with good Data Quality standards to find and retrieve information.
In terms of activities, Data Governance provides recommended processes for sharing, securing, and using data. Data catalogs support Data Governance needs by connecting datasets and giving enough information to understand how to get and use data assets to solve business problems.
Benefits of Data Catalogs
Data catalogs promote information sharing, improve operational efficiency, and support discovery. They function as a “communication mechanism” that shares information across an organization and aligns an organization as to what its data assets mean, where they come from, and how this information relates to business goals.
Consequently, good data catalogs deliver benefits, including:
- Enhancing Data Quality: Trust and confidence in data due to agreement around Data Quality metrics
- Assuring compliance and security: Assurance of compliant and secure data through access
- Tracing data lineage: Data traceability through metadata management, which reveals data ownership
- Publicizing data availability: Users can determine whether they can access datasets and datasets status
- Using an intuitive interface: Access for all users, through an intuitive interface, to discover and apply data sources for their work
- Increasing digital transformation: Elevating Data Literacy in marketing, sales, and operations to successful digital transformation
In addition to their communication benefits, data catalogs improve operational efficiency. They do so by including and using technologies that expand the catalogs’ functionality, flexibility, and intelligence. Examples include:
- Quicker data access: Readily accessible datasets due to faster data computing and more efficient storage
- Easier data asset discovery: Active metadata in the system flags corporate-wide available critical datasets
- Targeted searching: Filter and drill down capabilities to get descriptions, lineage, and understanding of retrieved datasets
- Faster discovery of data relationships: Notifications of related datasets for a topic based on profiling and tagging
- Better findability: More relevant data classification that fits the scale of search parameters
- Efficient administration: Automated services to extract metadata, tag and classify data, improve Data Quality, and map business glossary terms to technical data assets
The newer smart data catalogs that use generative AI, a pattern recognition application used to generate new content, provide additional benefits, such as:
- Richer metadata: Leveraging data available in a large language model (LLM) to enrich metadata
- Timely administration notifications: Notifying and advising Data Governance to find a data steward for a view or report
- More creative problem solving: Recommending related or new data to explore, based on identification of new relationships, to solve a business problem
- Quicker anomaly detection: Quicker detection of anomalies within the metadata
- Better Data Quality remediation: Correcting Data Quality and preparation issues with the metadata
Evolution of the Data Catalog
Data catalogs have roots in the old library card catalog, providing metadata for users to research topics and find books or other documents in a library. Additionally, card catalogs provided metadata context about library materials like subject area and standardized what metadata was provided and how.
As database management systems became available, technical professionals needed to understand the structure of that data to find information, run reports, maintain applications, and fix database-related errors. So, engineers created data dictionaries that functioned from standardized technical metadata as repositories for database schema, sometimes accompanied by business documentation around the database tables and columns.
However, in the early 2000s, the volume, variety, and speed of data created and made available increased significantly, resulting in big data. To compute and store this data, many organizations turned to cloud computing, outsourcing services to house and provide big data and its metadata more efficiently.
Consequently, companies found less time to find and get insights from data, making it too cumbersome to ask IT or engineers alone to do these tasks. In the 2010s, data catalogs evolved by adding business metadata to enable professionals to search the data based on its practical meaning and access it directly for their work.
Data catalogs in the 2020s use AI and machine learning (ML) to enrich existing records about a dataset through active metadata that is obtained in real time. Generative AI recommends more relevant datasets for their analysis. Data catalogs will continue to evolve in the marketplace to where a person will discover new insights in one place from datasets across multiple industries.
Different Types of Data Catalogs
Organizations can customize their data catalogs, choosing functionalities to simplify work tasks and the catalog’s technical engine. Some aspects to consider include:
Open vs. tightly controlled: Open catalogs operate like wikis and foster collaboration. Anyone can add descriptions or notes and review or suggest updates to catalog entries.
Tightly controlled data catalogs have more curation and approval processes built. As a result, they narrow roles with access to and maintenance of catalog entries, promising more robust security processes and legal and regulatory compliance assurance.
Open source vs. Data Catalog as a Service (DCaaS): An open-source catalog, available at a low or no cost, provides opportunities for companies to customize their platform and features. However, companies must have very skilled technical talent to develop and maintain the catalog.
While a DCaaS costs significantly more, it outsources catalog administration dedicated customer support. Also, organizations can take advantage of advanced features. So, corporations can focus on their work and leave any data catalog infrastructure maintenance to the pros.
Cloud vs. on-premises and technology stacks: A data catalog must integrate with systems computing and storing business data assets. Often, to do so requires accessing cloud-based resources, like Amazon Web Services (AWS) or Microsoft Azure. Cloud-based data catalogs would work well should an enterprise store or compute its data in the cloud.
On-premises data catalogs connect to data systems developed and maintained internally. While this approach may cost more, firms have more control to secure the data catalog from unauthorized access using a firewall.
Machine learning tools vs.generative/AI: Data catalogs with ML tools automate data classification and discovery processes. They also simplify tasks with metadata, like tagging, lineage, and curation.
Smart data catalogs that leverage generative AI go beyond typical ML tooling. They streamline administrative tasks by enriching entry metadata and providing descriptions and synonyms. Also, these catalog types suggest alternative queries and better handle natural language when business professionals run searches.
Data Catalog Use Cases
Various businesses have used data catalogs to evaluate metadata, trace data, and classify data for findability. Additionally, data catalog use cases demonstrate improvements in accessing data.
Some examples include:
- The World Bank designed a data catalog to make its “development data easy to find, download, use, and share.” See the screenshots above.
- GE Aviation used a data catalog to unify its data sources and make them more accessible to users across the organization through a self-service initiative.
- An organization’s marketing team wanted to customize campaigns to support, cross-sell, and upsell. A data catalog solution provides a way to review data assets to achieve this goal.
- Brainly, a peer-to-peer network of questions and answers to help students with homework, implemented a data catalog as a Data Governance project to make data more discoverable and sharable among teams.
- The Associated Press used a data catalog curated to bridge relevancy and reusability. The organization doubled its data production and customers’ data usage.
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