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Get the reportThink of data search like Google for your company's data – it's how teams find the exact information they need when they need it. Without strong search capabilities, finding the right dataset or dashboard becomes like searching for a needle in a haystack, especially when organizations have thousands of data assets spread across different tools and systems.
Strong search features transform how teams work with data in three main ways:
Speed to insight: Instead of spending hours hunting through different tools or asking colleagues where to find things, teams can quickly search and find what they need. It's the difference between spending 30 minutes looking for a dashboard versus 30 seconds finding it through search.
Prevention of duplicate work: When teams can easily find existing work, they don't waste time recreating analyses that already exist. For example, if someone has already built a customer churn analysis, others can find and build upon it rather than starting from scratch.
Data confidence: Good search doesn't just help you find things – it helps you trust them. When searching, teams can immediately see who owns the data, when it was last updated, and how others are using it, making it clear which sources they can rely on.
In short, search is the foundation of effective data discovery, making the difference between a data team that spends most of its time looking for data versus one that spends its time using it.
Secoda delivers enterprise search functionality powered by advanced AI and natural language processing, providing comprehensive search capabilities across all data assets and metadata. This approach makes data discovery intuitive and efficient, particularly in complex environments where finding the right data quickly is crucial for business operations.
Key features:
Atlan delivers search functionality powered by Elasticsearch and Elastic's query DSL, providing search capabilities across all data assets and metadata.
Key features:
While both platforms provide comprehensive search capabilities, they differ in their underlying architecture and approach:
Search technology: Secoda is built on AI and natural language processing, while Atlan is powered by Elasticsearch and Elastic's query DSL.
Query handling: Both platforms support keyword search with fuzzy matching, partial matches, and case-insensitive exact matching. Secoda extends these capabilities with additional search methods: a conversational AI chatbot for exploratory searches, and access to previously asked data questions.
Results and context: Secoda provides AI-powered rankings with personalization, data quality scoring filters, and includes context-rich results showing metadata, quality metrics, and usage statistics. Atlan offers customizable displays and multiple sorting options.
Knowledge sharing: Secoda integrates previously asked and answered data questions into search results, reducing redundant queries, while Atlan focuses on traditional search results.
Filtering capabilities: Both platforms offer comprehensive filtering by source, domain, ownership, tags, verification and properties. Secoda additionally allows filtering by data quality scores to surface only high-quality assets.
The choice between Secoda and Atlan's search features depends on your specific needs:
Secoda is best for organizations that:
Atlan may be more suitable for organizations that:
When evaluating search capabilities, consider your specific requirements around natural language processing, the types of search patterns you need to support, and how important features like personalization, data quality filtering, and access to historical data questions are to your workflow. Both platforms offer comprehensive search capabilities but take different approaches to implementation and user experience.
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