Empower every team to find and understand data instantly with AI-powered search and automated documentation.
Secoda automatically ingests metadata from across your stack to give you a single view of your entire data landscape.
Find data as easily as searching the web. Secoda's intelligent search turns thousands of scattered data assets into an organized, searchable knowledge base. Teams gain immediate access to trusted data sources, see who owns them, and understand how they're being used—eliminating duplicate work and boosting confidence in data-driven decisions.
Create and maintain a business glossary that ensures consistent terminology across the organization. Define, standardize, and share data definitions that bridge the gap between technical and business teams.
Secoda provides a centralized location for collecting and organizing data requests so data teams never have to answer the same question twice.
Empower your team with automated lineage, certified assets, and governance workflows—ensuring everyone has reliable, well-documented data at their fingertips.
Give your team insight into your data relationships with automated column and table level lineage functionality. Add any additional context to lineage using the Secoda API and notify others when lineage changes impact their work.
Certify and share commonly used data assets for internal access. Secoda's AI data catalog identifies frequently used data and sensitive resources, empowering confident data usage within the company.
With Secoda, data teams save time and ship faster by making it easier to work together.
Multiple users can collaborate on the same documentation, data cataloging, or data tagging task in real-time with an AI-powered data catalog tool
Secoda integrates with Git so you can easily track changes, collaborate, and ensure data integrity.
Teams, collections, and documents make it easy to curate and organize your data knowledge
Admins can grant appropriate data access to individuals and teams to ensure each business unit sees only what they need to.
"Secoda AI lets me reduce the time my team spends on documentation by 90%"
Tidiane NDIR
Chief Data Officer
Secoda upholds industry-leading security standards while being user-friendly, fast, and intuitive.
Built for data teams, designed for everyone so you can get more from your data stack.
A data catalog is used as a centralized repository or inventory of metadata about various datasets within an organization. It provides a structured and organized way to manage and discover data assets. Data catalogs help users understand what data is available, its attributes, quality, lineage, and usage, facilitating data governance, collaboration, and efficient data discovery for analysis and decision-making.
Firstly, it should offer robust metadata management capabilities, allowing teams to annotate and document data comprehensively. This metadata should include information about data lineage, quality, and usage, providing a holistic view of the data's lifecycle. See the full set of criteria in the Ultimate Data Catalog Buyer's Guide.
Creating a data catalog can greatly help you with organizing the data they collect, therefore making it easier to find what you need when you need it. The 5 steps involve: gathering sources, assigning owners to resources, getting support and buy-in, integrating workflows, and upkeeping the catalog.
AI data catalogs offer a wide range of benefits for data-driven businesses such as improved data discovery and management, data quality, data governance, and better insights.
An enterprise data catalog is a centralized repository that organizes and manages an organization's data assets based on their metadata. It provides a single point of reference for discovering, understanding, and using data at scale.
A data catalog helps analytics engineers easily discover and locate relevant datasets for their analytics projects, saving time and effort in searching for data. Secondly, the catalog provides essential metadata about the datasets, such as data definitions, schemas, and quality metrics, enabling engineers to understand and evaluate the data's suitability for analysis. Lastly, a data catalog promotes collaboration and knowledge sharing among analytics teams by providing a centralized platform to document, annotate, and share insights or best practices related to the datasets, enhancing the overall efficiency and effectiveness of analytics workflows.
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