Selecting the right data catalog is critical, yet organizations often fall into three major pitfalls. First, failing to prioritize connector support limits scalability and future integrations. Second, over-relying on automation in data curation results in metadata that lacks business context, making it difficult for users to derive value. Third, designing a catalog primarily for business teams ignores the distinct needs of data, operations, compliance, and governance teams, leading to poor adoption and fragmented workflows. A successful catalog must integrate strong connectivity, blend automation with human input, and support all key teams for enterprise-wide efficiency.
Many organizations begin with a lightweight metadata tool that meets their immediate needs but later realize it lacks the necessary connectors for broader integration.
Without seamless connectivity to critical systems—such as BI tools, ETL pipelines, and SaaS applications—scalability becomes a challenge over time, forcing companies to switch to a more advanced data catalog eventually.
A data catalog’s effectiveness depends on its ability to integrate with multiple data sources across an organization. The broader its connectivity, the more comprehensive its data landscape.
Applications – Business and SaaS applications generating transactional data.
Databases – Relational (e.g., MySQL, PostgreSQL) and NoSQL (e.g., MongoDB, Cassandra) databases for storage.
Data Warehouses – Large-scale analytical storage systems like Snowflake and BigQuery.
ETL Systems – Data pipelines (e.g., Apache NiFi, Talend, Informatica) that transform and move data.
BI & AI Systems – Analytics tools like Tableau, Power BI, and machine learning platforms.
Most companies first connect their data warehouses, since these store business-critical data. As data adoption matures, they expand integration to:
BI tools for reporting and analysis.
ETL systems for data movement and transformation.
Business applications to track transactional data.
While early-stage organizations may require only a few connectors, long-term success depends on scalable connectivity.
While early-stage organizations may require only a few connectors, long-term success depends on scalable connectivity.
A catalog with a limited integration framework will eventually create bottlenecks, hindering governance and analytics capabilities.
Read more: See how OvalEdge integrates with 150+ data sources
Automation has transformed metadata management by making it faster and more scalable. Features like automated lineage tracking and AI-driven PII identification reduce manual effort, ensuring metadata is captured efficiently.
However, relying solely on automation in data curation is a major pitfall—while it extracts and classifies metadata, it cannot provide the business context necessary for making informed decisions.
Automation-driven metadata curation primarily captures technical attributes—table names, column types, and system-generated relationships—but it fails to answer critical business questions, such as:
A "customer" table may be automatically cataloged, but automation cannot determine whether it contains only active customers, historical data, or prospects.
Without business input, analysts may misinterpret datasets, leading to incorrect insights.
A dataset supporting churn analysis and another supporting customer segmentation may look similar at a technical level.
Automation cannot distinguish their unique business applications, making it difficult for users to choose the right dataset.
Automated lineage tracking captures broad data movements, transformational logic but doesn’t capture how the source data is collected, what was the business process of creating the source data.
This creates blind spots in understanding data accuracy and reliability.
Without proper contextualization, even the most advanced automation can result in an underutilized, confusing data catalog.
To make a data catalog effective, business users must actively participate in the curation process.
To make a data catalog effective, business users must actively participate in the curation process.
A well-designed catalog should:
Organizations should treat automation as a scalability enabler—not a replacement for human expertise. The most effective data catalogs strike a balance between:
Automation for efficiency – AI-powered PII detection, automated lineage tracking, and metadata extraction reduce manual workload.
Structured Human input for trust – Business engagement ensures metadata is meaningful, reliable, and useful.
A catalog that integrates both improves metadata quality, drives adoption, and ensures long-term usability.
A common mistake in selecting a data catalog is focusing only on business teams while ignoring other key stakeholders.
While business users rely on the catalog for insights, it is maintained, monitored, and governed by multiple teams, each with distinct needs. A catalog that fails to address these needs will see low adoption and fragmented workflows.
A catalog that fails to address the needs of all key stakeholders. will see low adoption and fragmented workflows.
A data catalog must support five key teams:
Focus on searching, analyzing, and deriving insights from data.
Need intuitive search, AI-powered recommendations, and self-service data access.
Require integration with BI tools like Tableau and Power BI.
Build data products and work with raw and prepared data.
Require impact analysis, lineage tracking, and dataset comparison tools.
Need integrations with development tools and collaboration platforms like Slack.
Maintain data pipelines and ensure smooth operations.
Depend on real-time notifications, “watch lists,” and automated alerts for pipeline health.
Require tight integration with data quality monitoring tools.
Oversee regulatory adherence and data sensitivity.
Require audit logs, PII tracking, and compliance reporting.
Need features that generate automated regulatory reports.
Manage data policies and enforce compliance.
Require bulk-action capabilities for policy enforcement.
Need research tools to assess governance adoption.
Key features that enable multi-team adoption
To support these user groups effectively, a data catalog must include:
Advanced search & filtering – Extensive filtering, data lineage tracking, and business glossary terms.
Role-based access management – Secure data governance and controlled permissions.
Impact analysis & data comparison – Visibility into data dependencies and version tracking.
Real-time notifications & alerts – Keeping teams informed about schema changes and risks.
Audit & compliance reporting – Enabling automated PII and regulatory tracking.
Research & bulk actions – Allowing governance teams to manage multiple data assets efficiently.
BI & development tool integrations – Ensuring seamless workflows across teams.
Selecting a data catalog is a critical decision that impacts an organization's ability to govern, discover, and utilize data effectively. However, many businesses fall into 3 common pitfalls that undermine long-term success.
Ensuring broad connector support from the outset prevents scalability issues and avoids costly migrations in the future. Likewise, while automation accelerates metadata management, it cannot replace human expertise in providing business context—balancing both is key to creating a trusted and useful catalog. Additionally, a successful data catalog must serve the needs of all stakeholders, including business users, data teams, operations, compliance, and governance teams. A one-size-fits-all approach often leads to poor adoption and fragmented workflows.
By prioritizing comprehensive integration, blending automation with human curation, and ensuring cross-functional usability, organizations can build a data catalog that not only supports current needs but also scales with evolving data strategies.
A well-designed catalog is more than just a metadata repository—it becomes the foundation for effective effective data governance, compliance, and enterprise-wide data-driven decision-making.