Today, data no longer resides within organizations’ on-premises systems, but is rather scattered across various data systems, applications, cloud, and multi-cloud environments.
While offering agility, multi-cloud creates data silos and a lack of data governance, often requiring significant resources or automation tools to discover and classify data assets. This is where a modern data catalog becomes core to unlocking the true potential of data while ensuring comprehensive data visibility and regulatory compliance.
What is a Data Catalog?
As organizations scale at lightning speeds, data that was once restricted to a single database now travels across the globe across multiple data points, leaving a digital data footprint in multiple environments. This necessitates a robust modern data catalog system in place that enables organizations to obtain a detailed inventory of their data assets.
In essence, a data catalog is like a central data repository that hosts multiple data types in one place, enabling data owners or authorized individuals to access and leverage data for multiple business purposes, including data discovery and governance.
Understanding Modern Data Catalog
Legacy data catalogs that once dominated how organizations handled their data assets no longer provide a competitive advantage to organizations operating with hyperscale data volumes. They usually require manual input, are sluggish, lack data context, provide limited integration, and offer basic keyword search.
A modern data catalog, on the other hand, provides organizations with rich, dynamic metadata analysis that’s AI/ML-powered and harnesses the power of automation to help the entire organization. Unlike legacy data catalog that offers limited integration, modern data catalogs provide deep integration with the modern data stack, enabling comprehensive search capabilities across disparate data environments.
Modern data catalogs work in real-time in the background and evolve to accommodate regulatory updates. Once configured, they can automatically classify sensitive data, enforce access controls, making governance seamless, automated, and always current.
How Does a Data Catalog Work
Here’s how a data catalog process works:
Connectors search databases, lakes, warehouses, BI tools, and SaaS apps on schedules or all the time, pulling out technical metadata (schemas, tables, columns, file formats) and change events so that new or changed assets are collected automatically.
Business glossary words, tags, and definitions; operational indications like freshness, usage, and performance; social input like owners, comments, and ratings; and AI/ML that classifies sensitive data, finds relationships, and normalizes names all add to the raw stream.
C. Organizing a catalog
All metadata is put into a searchable repository and shown as a graph, showing how pipelines and BI objects are related to each other. Datasets are organized into groups based on domains and taxonomies, and governance principles (such as classifications, retention, and masking restrictions) are tied to assets.
D. Data Discovery
User interaction analysts, developers, and business users find data using natural language or faceted search, look at the context (source, quality, lineage, owners), see examples, and work together by leaving comments and endorsements, typically directly from their BI/analytics tools.
E. Access and Governance
Role- and attribute-based controls control who can view or use data. Sensitive fields are automatically hidden or redacted, access requests get quick approvals, and every activity is documented so that it can be audited and reported for compliance.
F. Continuous Updates
Incremental scans keep metadata current, identify schema and quality changes, recompute lineage as pipelines expand, and feed back into ranking and recommendations—creating a self-improving loop that maintains the catalog accurate and trusted.
Benefits of Using a Data Catalog
Data catalog comes packed with several benefits. Here are some of them:
A. Improved Understanding of Data
Since a data catalog brings together all data types in one unified place, it provides organizations with an improved understanding of what data is available at hand.
B. Increased Operational Efficiency
Data catalog enables swift data discovery, enabling data owners to save time by leveraging data for improved insights rather than spending time searching for data.
C. Reduced Risk of Data Breaches and Compliance Violations
Data privacy regulations mandate organizations to have a comprehensive understanding of their data assets and discard any unnecessary or outdated data. A data catalog helps organizations with automated sensitive data classification, ensuring regulatory compliance and identifying data that requires additional safeguards against data breaches.
D. Enhanced Success in Data Management Initiatives
Data can get lost and reside in silos in today’s modern-day data management services, particularly in cloud environments. Additional cloud migrations can further complicate who the data belongs to, data type, its residency, etc. A data catalog significantly minimizes the risk of hidden data silos or data blind spots that make it challenging to identify the data owner.
Metadata is at the core of a data catalog, and different types provide different uses:
Technical metadata details the physical structure of data assets, including databases, schemas, tables, and columns. Technical roles benefit from their ability to comprehend important aspects of data asset deployment, like data formats and types.
Business metadata provides a data asset's business context and organizational relevance, enabling both everyday data users and data specialists to easily interpret this information.
Operational metadata explains how the data item was created and used. For instance, it contains details on who has accessed, utilized, updated, and altered it, as well as when and how.
Challenges a Data Catalog Can Address
Business users, data consumers, data scientists, and those responsible for privacy, security, and governance find it challenging to understand not just the location of all data inside an organization, but also its context and appropriate usage.
Organizations want a simple method to understand what information they have, especially sensitive data, where it is kept, the rules governing it, and its significance. Data catalog addresses several pain points most modern-day businesses face. Challenges include:
A. Siloed Approach to Data Discovery & Classification
With data scattered across cloud providers, SaaS apps, and on-prem systems, it is challenging to discover all the data that exists, who owns it, and where it is located, including structured and unstructured systems. Organizations also waste time and fail to integrate the data already existing in their environment because they are hardly aware of its existence.
B. Data Discovery & Classification Isn’t Enough
Data is projected to grow significantly, potentially reaching 394 zettabytes by 2028. Additionally, it is predicted that private, hybrid, multi-cloud, or SaaS clouds will house 50% of all data, necessitating the need for building a catalog of all shadow and managed data assets.
C. Contextual Data Intelligence
Businesses require deeper contextual intelligence of their data assets to make informed decisions and honor data subject requests. Ultimately, it provides businesses with a clear understanding of what data is available where, its sensitivity and whether it is protected. This helps ensure compliance with evolving laws such as the GDPR, CCPA/CPRA, HIPAA, etc.
D. Data Governance and Lineage
There is no governance and lineage tracking without clear visibility of data assets. Data catalog helps organize data, enabling organizations to understand their data's origin, data flows, transformations, and usage across geographies. This helps in effective data governance and lineage tracking.
Best Practices for Data Catalog Implementation
Successfully implementing a data catalog involves various approaches, including:
A. Gaining Visibility Across Your Data Stores
Begin by assessing all data sources and connecting a map of where the data has traversed.
B. Build a Unified Data Language
Align all stakeholders across the organization on the same data language to ensure consistency and avoid complications and repetitive data.
C. Conduct Data Quality Assessment
Ensure datasets contain accurate data records and are maintained in a secure database.
D. Understand Data Lineage
Gaining visibility into data lineage is crucial to tracking how data flows and transforms across data pipelines. It provides transparency for any gaps and demonstrates compliance.
How Securiti Can Help
Securiti Data Catalog provides organizations with the ability to collect and store all the information around all their corporate data. It enables users to automatically leverage data discovery and classification to build structured and unstructured data catalogs that are always up to date.
Securiti Data Catalog is a holistic solution that makes it easier for users to find datasets and review security and privacy metadata to determine the data’s appropriateness for their project.
To learn more about Securiti’s Data Catalog, schedule a demo now.
